Introduction
Many professionals in the product world are today talking about the use of artificial intelligence in design, development, and building features.
But the strange paradox?
Very few talk about the most important stage of all: Product Research.
And this is precisely where the battle is won.
The quality of research determines:
The accuracy of decisions
The efficiency of design
The level of business risks later on
With AI tools entering strongly into every stage of product design, it has become essential to have a clear, intentional, and non-random research approach — not relying on "just ask ChatGPT and that's it."
In this article, we present a practical framework of 3 stages for using artificial intelligence in product research without falling into the trap of superficial decisions.
Stage One: Start with a clear Research Brief (not random)
Why is this important?
Artificial intelligence does not think... it responds to context.
If you do not provide it with a clear context, it will give you beautiful but inaccurate answers.
You do not need a 20-page academic document.
One page is enough — but it is crucial.
What should the Research Brief contain?
The product or feature under study
The research objective (the decision you want to make)
The target users and context of use
The research stage (exploratory, concept testing, usability, post-launch)
The constraints and assumptions
The potential risks if the research is wrong
This Brief not only serves you...
But it becomes the reference mind that all AI tools will work on later.
Stage Two: Turn raw data into a knowledge base — without summarising it.
The common mistake
The team gathers:
Interviews
Surveys
Notes
Analyses
Then you throw it all into ChatGPT and ask for a summary.
With this one action…
You close the door to discovery.
Because:
Summarising kills the "unknown unknowns"
Unknown unknowns
What is the correct alternative?
Gather everything as it is: messy, unorganised, raw
Clean the data from the old and unrelated
Do not ask for a general summary
Provide the original sources to the AI
The ideal tool here: NotebookLM
Why?
It only works on the sources you provide it with
You have full control over the data
It allows for linking ideas and sources
Over time, it turns into a research knowledge vault
In this way, AI does not "think" on your behalf,
but opens up patterns you would not see.
Also read: Strategic Product Planning in the Age of AI
Stage three: Turn research into design-ready outputs
Understanding alone is not enough.
True value begins when research turns into actionable decisions.
At this stage, AI can help you generate:
Opportunity Statements
How Might We questions
Evidence-backed Problem Statements
Personas based on real data
Insight → Implication → Opportunity tables
Here, AI saves the most time.
But — here is the important warning —
Do not let it drive the car.
Do not use artificial intelligence to:
Determine “what is right”
Replace empathy with the user
Make sensitive ethical decisions
Interpret human context alone
Your role remains essential in:
Formulating the research question
Choosing the right data
Interpreting the fine details
Balancing options
Artificial intelligence is a helper…
Not a product manager.
In summary: from AI Tool to Research Partner
Superficial use of artificial intelligence in research turns it into:
A sleek text generator
Smart use turns it into:
A systematic thinking partner
When:
You start with a clear context
Build a correct knowledge base
And turn results into design decisions
You do not “use AI”…
But multiply your capacity as a researcher and producer.
🚀 with Echo Media
AtEcho Media, we do not believe in using artificial intelligence as a “lazy shortcut.”
We believe in it as aLever for thinking and decision-making.
If you are:
Working in UX or Product
Wanting deeper research and clearer decisions
And looking for practical application, not theory
Follow Echo Media
And build products designed with the mind… not by intuition.