The Heart and Brain of the AI Research
Summary
As a designer research working on Conversational AI products, I’ve been collaborating with AI Researchers (Computer Scientists) and ML engineers. The cross-pollination of our two research disciplines – i.e., I bring my design thinking, storytelling, qualitative research and thick data analysis lens, while the Computer Scientists bring their logical reasoning, modeling and coding, and big data analysis lens – has resulted in a much smarter and more empathetic AI product, as well as innovations in the Cognitive AI domain. I’ll share three use cases of how we human researchers collaborate with the AI researchers and the lessons learned.
Key Insights
-
•
UX research often stays on the surface of usability, missing deeper impacts related to data and AI models beneath the iceberg.
-
•
Natural language processing models require extensive and specific training data to understand diverse user utterances.
-
•
AI systems struggle to identify causality, often confusing correlation with cause and effect in user queries.
-
•
Users often engage in investigative dialogue before reaching their true question or desired outcome.
-
•
Human researchers can pinpoint why users interact with AI in unexpected ways by analyzing behavior beyond simple metrics like button clicks.
-
•
Collaboration between human researchers and AI scientists through shared observations and joint sense-making enhances AI development.
-
•
AI planning, such as the monkey and banana problem, offers a framework connecting user goals with efficient AI action sequences.
-
•
Human-centered research can inform training data selection, helping AI models to better predict and respond to user needs.
-
•
Effective AI product research requires understanding the technology enough to communicate meaningfully with technical teams.
-
•
Human researchers increase impact by shifting from passive observers to active participants in ideation, design, and strategy discussions.
Notable Quotes
"After more than 10 years of doing UX research, I was still only working above the iceberg, focusing on the usability and feature level."
"Machines can identify correlation via association, but it’s extremely difficult for them to reason and identify causality."
"People don’t always get straight to their question; they might need to do some investigation first before they figure out what to ask."
"The AI planning monkey and banana problem made me question whether getting the bananas is really the end goal."
"By identifying the triggers that lead customers to ask questions, I helped data scientists figure out what training data to explore."
"It’s not enough just sharing customer stories and insights; we need to get into the weeds and collaborate with product and tech partners."
"We need to be genuinely curious not just about our customers, but also about our colleagues and the technology behind AI."
"Our foundational interview, observation, and sense-making skills are evergreen and indispensable in the AI world."
"To be truly impactful, human researchers need to actively participate in product design sprints and strategy meetings."
"This emerging AI technology has given us a golden opportunity to combine the heart and brain of technology and make meaningful impact."
Or choose a question:
More Videos
"One of the amazing things about becoming very successful leaders is eventually becoming obsolete."
Anna Avrekh Dr. John Pagonis Klara Pelcl Sina SchreiberExpert Panel: Leading in and with Research
March 10, 2022
"Our truth is no longer singular; transparency and comprehensive storytelling are more important than ever in research."
Taylor KlassmanShaping the Next Era of UX Research: Collaborative Forum
March 11, 2025
"We see value-based research strategies emerging that center equity and ethics at the highest levels."
Dr. Jamika D. Burge Steve Portigal Alba Villamil Sam LadnerThe Future of Research: Bridging the Gaps
July 29, 2021
"Compensation is so important to thank people for their time, and you will definitely build your recruitment pool much faster if you offer it."
Marisa BernsteinIt Takes GRIT: Lessons from the Small, but Mighty World of Civic Usability Testing
December 9, 2021
"A lot of people think about design systems as a project, but they’re really about how an organization gets work done."
Dan Mall“Ask Me Anything” with Dan Mall, Author of Upcoming Rosenfeld Title, Design that Scales
October 2, 2023
"We need to create learning frameworks that allow researchers to be effective, even without big tech pedigree."
Chris GeisonTheme 1 Intro
March 9, 2022
"Using the inverted pyramid doesn’t mean dumbing down your findings, it means explaining complex issues clearly."
Bruce GillespieLearning from journalism: Balancing impactful communication with compassionate storytelling
March 13, 2025
"We Westerners are designing for ourselves without realizing it, feeding back into a loop of luxury products."
Nancy DouyonWe'll Figure That Out in the Next Launch: Enterprise Tech's Nobility Complex
June 15, 2018
"Uganda is Africa’s largest refugee host nation with over 1.5 million refugees, 60% of whom are children—yet this crisis is often invisible in media."
Liz EbengoThe Burden on Children: The Cost of Insufficient Post-Conflict Services and Pathways Forward
December 4, 2024
Latest Books All books
Dig deeper with the Rosenbot
What strategies help UX teams translate usability findings into stakeholder-relevant outcomes like cost savings or risk reduction?
How does systems thinking help identify leverage points for improving rural maternal health?
Why is human storytelling stewardship still essential despite advances in AI and research democratization?