The researcher of the future is a sense maker

As marketing research analysis gets offloaded to AI, where do human researchers add value?

Editor’s note: Tanya Pinto is a principal UX researcher at Microsoft, an AI researcher, brand strategist and social-impact founder who turns insights into strategy and growth. Across 25+ years, she has worked with world-class enterprise technology brands alongside a deep commitment to humanitarian work as founder of Baal Dan Charities. Find Pinto on LinkedIn.

For much of my career, I thought becoming a better researcher meant getting better at the mechanics of research: asking sharper questions, conducting stronger interviews, designing rigorous studies and identifying patterns in the data. I’ve had some insecurity about my lack of quantitative and statistical research skills.

I still believe those skills matter. But as AI accelerates skill and capacity, I’ve come to believe that another skill matters even more: knowing what to make of what we find.

AI is changing how quickly researchers can organize information, explore data and synthesize large volumes of feedback. As more of the mechanics of research become faster and easier, our profession faces an important question:

If analysis gets offloaded to AI, where does the researcher add value?

I believe the answer is sensemaking.


From collecting data to finding meaning

Researchers have more information available to them than ever before. We can combine interviews, surveys, behavioral signals, secondary research, customer feedback and countless other sources.

But more information does not automatically create more understanding.

Some of the most interesting insights I have encountered in my career were not necessarily the most commonly mentioned observations. Sometimes they emerged from contradictions. Sometimes from an outlier. Sometimes from a participant doing something completely unexpected.

Those moments remind me why human interpretation remains so important.

AI can help us identify patterns and organize information. But researchers still have to ask:

• Why does this matter?

• What might explain it?

• What are we missing?

• What should be done differently because of what we learned?

Our value increasingly lies in connecting the dots.

From studying behavior to studying adaptation

Traditionally, researchers spend a great deal of time asking, “What are people doing?”

Increasingly, I think we also need to ask, “What are people learning to do differently?”

This is especially important when studying rapidly changing technologies.

People rarely adopt new technology exactly as its creators imagine. They experiment. They develop workarounds. They combine tools. They abandon old habits. They invent new ones. This is at the heart of many UX research studies I have led.

We need to start looking at both adoption and adaptation.

One simple framework I use when examining emerging behavior is to look for three things:

• What are people doing?

• What are people inventing or working around?

• What are people no longer doing?

The second question is particularly revealing. When people start inventing their own processes, language or ways of working, they may be signaling a need. And the edges of behavior can foretell future uses and scenarios.

From reporting findings to creating conviction

There is another shift researchers need to make.

We must move from delivering findings to creating understanding.

I spent more than 13 years in branding and advertising. In advertising, brands must have conviction, consistency and connection. This framework needs to be applied to research reporting and storytelling with the rigor I was used to in my years of advertising pitches and presentations.

What does my audience need to understand? When we approach reporting this way, it fundamentally alters how research is communicated.

A report can contain 50 findings and all the reams of data possible with AI outputs and still have very little impact if it does not inform key decisions. Similarly, if reporting gets overly summarized, it loses the context and meaning needed for a complete view that is necessary for effective decision-making. Neither extreme is right.

Today I think about research storytelling using the data to connect to the larger problem or opportunity. And most importantly, what decisions or recommendations can be made as a result of investing in the research.

A researcher’s responsibility cannot end with, “We found X.” Increasingly, we need to help inform how we should be thinking differently or discerning what matters as a result of the research.

The future belongs to sense makers

AI will undoubtedly change research. It can accelerate parts of analysis, synthesis and information gathering. But I don’t believe that makes researchers less important. It makes judgment more important.

The researchers who thrive in this environment will not simply be the people who collect the most data or produce the most comprehensive reports. They will be the people who can look across imperfect, fragmented and sometimes contradictory evidence and explain what it means.

Organizations rarely suffer from a shortage of information. They suffer from a shortage of clarity.

And that may ultimately be the researcher’s most important role: turning evidence into understanding, understanding into action and complexity into clarity.

The researcher’s new superpower will be in making meaning.