You’ve probably seen the claims: “AI can do your customer research now.”
Drop a prompt into ChatGPT, pull a summary from Perplexity, or feed a few links into Deep Research and generate a full report in minutes. Citations included.
It’s quick. It feels useful. But it’s not research. It’s a shift in expectations—where research is treated as something to automate, rather than a discipline to practise.
We’re all researchers now, in the everyday sense of the word.
You Google to compare flights. Ask Reddit for laptop recommendations. Skim Trustpilot before buying that supplement someone mentioned on Instagram. Maybe you ask Perplexity to summarise a trend or get ChatGPT to explain something in half the time it’d take to read a blog.
In those moments, the tools work well enough. We want an answer and we usually get one.
This habit—searching for quick, packaged information—has started to shape how teams approach business research too. The result is a shift in mindset.
Research, once seen as a process of exploration and synthesis, is now treated more like a transaction: pose a question, get an answer, move on.
It's not just that more people are doing research. It's that the research itself is becoming thinner. The speed and accessibility of information can create a false sense of clarity. We start with the answer we want, find data that supports it, and call it insight.
This is especially common when decisions are fast-paced or under pressure. But when that surface-level approach becomes the default—used to validate messaging, define customer needs, or direct product strategy—it limits what’s possible. Businesses don’t just need information. They need structure, context, and challenge. That’s what leads to insight and innovation.
When we need to solve a marketing challenge, it’s easy to fall into the pattern:
On the surface, it looks like the right steps are being taken. There’s information. There’s output. But if there’s no direct input from customers, no synthesis, and no validation—it’s not research. It’s repackaging.
One of the biggest risks in DIY research is over-relying on secondary sources. You're basing decisions on what’s already out there—someone else’s questions, someone else’s audience, someone else’s goals. The data may be accurate, but it’s not specific. It won’t tell you what matters most in your business.
If everyone has access to the same information, there’s nothing strategic about it. It might help you feel informed, but it won’t help you stand out. More importantly, it won’t tell you what to do next.
This kind of surface-level input often reinforces what teams already believe. We search until we find something that supports our hunch, then move on. It’s not research—it’s confirmation, dressed up as insight.
AI tools can be helpful. They can organise information, speed up early-stage work, and save time on repetitive tasks. They’re good at pulling together summaries, highlighting patterns, and even suggesting themes across large datasets.
But AI doesn’t know what’s missing. It doesn’t weigh relevance or understand nuance. It can’t challenge assumptions, spot tension, or sense when something feels off.
AI treats all inputs as equal—whether it’s a peer-reviewed report or a low-quality blog post. It formats outputs in a way that feels polished and complete, even when key gaps remain.
And yet, because the format looks professional and the tone sounds credible, it creates confidence. The kind of confidence that skips reflection and moves straight to action.
This is where the illusion of knowledge takes hold. Easy access creates a false sense of clarity. But without knowing what data was excluded—or how it was interpreted—your decision-making is built on unstable ground.
So far we’ve looked at how surface-level research habits have taken hold—and how AI can accelerate, rather than solve, the problem. Now let’s take a closer look at what experienced researchers actually bring to the table.
Strategic research isn’t just about collecting information. The thinking that happens before, during, and after is what makes the process valuable.
Here’s how skilled researchers work:
Clarifying the real question: Is this about improving a feature, or understanding why adoption is low? Are we testing messaging, or uncovering how customers frame the problem? Defining the right question is half the job.
Choosing the right methods for the goal: It’s not “let’s run a survey.” It’s: who do we need to hear from, and what’s the best way to get insight we can act on?
Mapping the assumptions: What are we taking for granted internally? What do we think we know? A good research process puts assumptions on the table early—so we can check them, not build on them blindly.
Finding tension: What did people say that doesn’t quite fit? What doesn’t add up? Where are different teams interpreting the same data in different ways? Researchers look for the edges—not just the themes.
Turning insight into decisions: Research that sits in a slide deck isn’t useful. The value comes from translating what we heard into direction: priorities, trade-offs, positioning, roadmap choices.
These aren’t just research skills. They’re strategic ones. And they’re hard to replicate without experience.
AI can be a helpful part of the research process—especially when you're trying to move faster or make sense of a lot of input. But it’s not a substitute for strategy, context, or decision-making.
Here’s how to think about what to automate—and what still requires people with the skills to interpret and apply what they’re seeing.
|
Use AI to... |
Use humans to... |
|---|---|
| Draft interview or survey questions | Define the right research question for your context |
| Spot biased phrasing or leading language | Decide what needs to be explored in more depth |
| Group qualitative feedback into themes | Interpret contradictions and tension |
| Summarise desk research and existing reports | Prioritise what matters for your product or strategy |
| Synthesise notes and transcripts | Sense what’s missing or unspoken |
| Speed up admin and documentation | Connect findings back to business goals and decisions |
There’s no shortage of data or content anymore. That’s not the challenge.
The challenge is knowing what to trust, what to ignore, and what to dig into further. That still takes time, skill, and people who are willing to look beyond the surface.
The teams who get this right aren’t the ones who move slow—they’re the ones who move forward with confidence, because they’ve done the work to understand what matters.
Insight doesn’t come from having the fastest answer. It comes from asking better questions—and being willing to sit with what you find.
And if that sounds slow? Just wait until you’re rebuilding a strategy based on shallow input.