In February 2025, The Economist published a telling headline: “The danger of relying on OpenAI’s Deep Research.” It wasn’t clickbait, it was a warning.
The article examined OpenAI’s promising tool, capable of parsing long documents, synthesizing multi-source data, and delivering research-grade answers in minutes. For many business leaders, it sounded like a miracle: instant market research, without the consultants. But as the article revealed, Deep Research was not a silent genius, it was an overconfident intern.
The tool misquoted official data. It confused economic sources. It hallucinated numbers, cited superficial blogs over peer-reviewed material, and often sounded right while being wrong. In one documented case, it listed only 69% of relevant data points when asked to map a cybersecurity vendor’s products. Worse, it missed an entire strategic initiative (OpenAI’s o4 project), even though it had been announced at Davos weeks earlier.
These aren’t minor glitches. In business research, a missed insight isn’t just an error, it’s a misdirection.
And yet, tools like Deep Research continue to be adopted as if they were replacements for domain expertise, strategic reasoning, and qualitative depth. This is the quiet trap organizations are falling into: equating automation with understanding.
The Illusion of Knowing: When AI Amplifies Overconfidence
One of the most dangerous shifts in the age of generative AI is not technological, it’s psychological.
Recent studies show that non-experts using AI tools often experience a false sense of expertise. Microsoft Research found that people who felt more confident in ChatGPT’s answers actually thought less critically about the topic. This is the Dunning-Kruger effect, reloaded: quick, confident answers from AI tools create the illusion of competence in users who don’t know what they don’t know.
The result? Overconfident decisions. Misinterpreted data. Strategic misfires.
This is especially dangerous in marketing, UX, and business strategy, where contextual nuance, cultural signals, and customer psychology shape the difference between a relevant insight and a tone-deaf campaign.
You don’t need someone who can find data. You need someone who can smell when the data is lying.
The Question Is the Strategy
Generative AI thrives on prompts. But the value of research doesn’t start with the answer, it starts with the question.
This is where many businesses slip. They believe that feeding a tool the right input is enough. But in reality, formulating the right research question is a strategic act. It requires understanding the market tension, the emotional levers behind consumer behavior, and the subtleties of what actually matters to uncover.
AI can mimic frameworks. But it doesn’t know what to challenge. It won’t ask: “Are we solving the right problem?” Or “Why aren’t users behaving the way we expected?”
That’s the difference between generating a report and unlocking an insight.
At Infinite Stair, we’ve seen it repeatedly: the most impactful research doesn’t just answer things better, it reframes what’s worth answering in the first place.
Why Qualitative Research Still Belongs to Humans
Large Language Models like GPT-4 can do many things, but deep qualitative research is not one of them.
In comparative studies, AI was able to identify surface-level themes from interview transcripts, but missed emotional subtext, failed to connect cross-interview patterns, and struggled with low-frequency but important signals. Human analysts, in contrast, detected subthemes, contradictions, non-verbal cues, and culturally coded responses with far greater precision.
Even in hybrid methods, where qualitative and quantitative data are triangulated, AI cannot yet perform reliable integration without risking conceptual mismatches. The limitations aren’t just technical, they’re cognitive. As researchers put it: “LLMs lack confirmability, credibility, and context sensitivity, all pillars of qualitative trustworthiness.”
And this matters, because human behavior is rarely explicit. Great researchers don’t just record what people say, they interpret what people mean, feel, and avoid. That requires judgment, empathy, and intuition. No model has those yet.
Insights Live Beyond Language
AI hears words. Humans read the room.
In usability labs, in-depth interviews, and ethnographic studies, meaning isn’t just verbal. It’s the pause before an answer. The microexpression of discomfort. The contradiction between what’s said and what’s implied.
Current AI models don’t detect these gaps. They can’t interpret nervous laughter, cultural body language, or shifts in tone that hint at hesitation or doubt. And yet, those subtle cues are often where the breakthrough insight lives.
This isn’t a matter of future model training. It’s a matter of human perception, of empathy, attunement, and intuitive pattern recognition. Skills that are cultivated, not computed.
Even the best prompt engineering can’t replicate being in the room when a user hesitates just long enough to tell you the real story.
When the Unexpected Happens, Only Humans Adapt
Research doesn’t always follow a script. Neither do users.
Sometimes, what derails a session is what reveals the truth: a participant goes off-topic and uncovers a hidden barrier; an unexpected silence shifts the conversation; a feature you thought was intuitive causes frustration no one predicted.
These aren’t bugs, they’re breakthroughs. But only if someone knows how to spot them.
AI, even at its best, is built for structure. It follows input patterns, learns from precedent, and extrapolates from what already exists. But business isn’t always repeatable. Markets shift. Audiences evolve. Feedback contradicts the brief.
When that happens, you need more than analysis, you need adaptation.
A human moderator knows when to pivot. A strategist recognizes when the original hypothesis no longer holds. A researcher can sense when the real insight is hiding behind a casual comment or a raised eyebrow.
In high-stakes environments, from user testing to brand positioning, what gives research its power isn’t just rigor. It’s responsiveness.
When research lacks that human adaptability, the cost isn’t just missed nuance, it’s missed direction.
The Real Cost of Misguided Research: It’s Not Just About Accuracy
If your research tool gives you a wrong number, you can fact-check it. But if it gives you the wrong direction, and you don’t know it, you may spend months building the wrong product, targeting the wrong market, or optimizing the wrong funnel.
A growing number of failed initiatives trace back to misinterpreted data, superficial insights, or overreliance on generic AI outputs. From chatbots misidentifying competitive signals to “personalized” ads that misunderstand emotional tone, the problem isn’t lack of information, it’s lack of expert interpretation. And it’s not just misdirection, it’s misjudgment. AI models trained on biased data or outdated sources can reinforce inequities or violate privacy expectations, sometimes without anyone noticing until the damage is done.
When AI Confuses Authority with Popularity
This is one of the quietest but most dangerous failure modes: AI tools often confuse what’s easy to find with what’s actually true.
In the FutureSearch evaluation of OpenAI’s Deep Research, the tool misquoted UK mortality data because it sourced from a company blog instead of the official Our World in Data repository. In another case, it hallucinated cybersecurity benchmarks by selecting outdated figures from an SEO-optimized site, ignoring peer-reviewed or government reports.
Why? Because most models are trained to prioritize accessible, high-ranking, or pattern-consistent content, not the most credible or recent. The algorithm doesn’t understand authority; it understands relevance by proximity, frequency, and familiarity.
But business strategy isn’t built on what’s popular, it’s built on what’s precise.
If your AI selects the wrong baseline, every decision that follows will drift off course.
You won’t just be wrong, you’ll be confidently wrong.
Expertise Isn’t Optional. It’s Your Margin.
Great research is not just a service. It’s a filter between noise and action.
At Infinite Stair, we work with clients who don’t just want dashboards. They want understanding of their users, their market, and the cultural signals shaping behavior. That means asking better questions. Synthesizing seemingly unrelated inputs. Making the leap from what’s said to what’s felt. And translating research into strategy, not just summaries.
AI can process data. It can accelerate workflows. But it can’t replace the craft of reading between the lines, or the strategic instinct that tells you what not to act on.
In an age of automated knowledge, judgment is your real differentiator.
Synthesis Is a Human Skill
AI can identify patterns. But synthesis, the art of connecting them across contexts, timeframes, and intent, still belongs to us.
Experienced researchers don’t just report what’s happening. They infer what it means, what it could become, and what levers are worth pulling next. They weigh contradictions. They notice what’s missing. They translate chaos into clarity.
In strategy, this isn’t decoration, it’s navigation. And no model, no matter how powerful, can replicate the years of industry sensemaking that sharpen a researcher’s intuition.
Because at the end of the day, it’s not about who has the data. It’s about who knows what to do with it.
Want to build deeper insight, not just faster reports? Let’s talk at Infinite Stair LLC.
Sources:
- The Economist (Feb 2025): “The danger of relying on OpenAI’s Deep Research”
https://www.economist.com/finance-and-economics/2025/02/13/the-danger-of-relying-on-openais-deep-research - Microsoft Research: Generative AI and Critical Thinking
https://www.microsoft.com/en-us/research/publication/generative-ai-and-critical-thinking-impacts-on-decision-making - FutureSearch Evaluation of Deep Research (Feb 2025)
https://www.futuresearch.ai/blog/evaluation-openai-deep-research-reliability-issues - Comparative Study on GPT-4 vs Human Qualitative Analysis (2025)
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375389/ - AI-Driven Data Triangulation – SINTEF, Sage Journals
https://journals.sagepub.com/doi/10.1177/16094069241231168 - Expert Judgment in Market Research – 2025 Meta-Evaluation Report
https://www.papergen.ai/blog/expert-human-judgment-in-ai-era-market-research-2025