AI vs Human: The Future of Market Research

Shobha Prasad discusses AI’s impact on market research. It provides faster analysis. It also enhances efficiency and offers deeper data insights.

The seductive voice of perfect language

“AI did a very good job… it articulated it so beautifully” — and that, Shobha says, is the danger. The system’s polish “has that hypnotic power” so a reader may accept the output without checking. This was learned the hard way in a blind test where the client chose the AI-written report over a junior researcher’s version. The result shocked the team because the AI hallucinated quotes and misattributed material. 

What happened in the blind test:
• AI produced highly readable, structured output.
• The client trusted the polished voice.
• But the AI also invented quotes and misattributed content.
• Humans detected gaps and depth AI missed. 

Why that polish is a pitfall

Shobha Prasad calls it “seduction.” The AI’s language reads like a finished product, which tempts readers not to dig deeper. “You get seduced by it and then you do not ask.” A human researcher invites interrogation. You can “grab the human researcher and shake them up” and say, “I do not believe what you’ve said here.” But you cannot do that to a printed AI report.

Where AI shines — the quick wins

AI is not a villain. Shobha lists concrete areas where it already excels:
• Fast transcription and summarisation. “Speed is brilliant.”
• Packaging and structuring — headings, subheadings, readable layouts.
• Desk research and synthesizing broad context. “It can bring you context that you do not have to spend hours searching for.”
• Image and review analysis at scale (e.g., Amazon reviews, picture diaries).

Where humans retain the edge

Shobha is blunt: qualitative depth, cultural nuance, and interpretation are human territories. Key human strengths:

• Ethnography and in-situ empathy. “We go into cultures… and spend time in the life of respondents.”
• Spotting contradictions and subtle cues that AI summarizes away.
• Translating lived context into actionable brand strategies. One example is the rural lantern study. It discovered that parents bought lanterns to support children’s education. That emotional metaphor became the brand’s positioning.

A practical comparison: AI vs human research

Task	AI (today)	Human researcher
Fast transcription	Excellent	Slow
First-draft synthesis	Very readable	Variable
Cultural depth	Often shallow; may hallucinate	Deep, contextual
Ethnography	Cannot be present in field	Essential
Scalability for large qualitative sets	Potentially huge (with prompts)	Resource-limited

How to use AI responsibly in research — practical rules

Shobha’s recommendations are precise:

• Treat AI as a junior researcher or co-pilot — not the final signoff.
• Provide rich background context to reduce hallucination. “Give feed that in as background information… so AI takes this into consideration.”
• Specify explicit specs. “Define every spec… so that AI does not interpret it differently.”
• Use AI where it adds clear value: transcription, summarization, desk research, synthetic personas, review analysis and image trend detection.
• Expect an iterative process: prompt engineering is a craft. “The art of prompt is important to learn.”

New methodologies: synthetic personas and scaling qualitative research

Shobha describes how AI can create synthetic personas from segment data. This enables ad testing or early idea screens. These screens are useful where primary research is infeasible. One example is hard-to-reach groups like people with serious illnesses. Shobha notes that these synthetic approaches can be “better than nothing” and sometimes “comparable to primary research.” But they need to be validated.

Ethics, trust, and the third creature

Shobha frames AI as neither human nor machine but “a new life form.” That idea matters for governance: it is a tool with its own characteristics. “If you believe like machines it will be accurate 100 percent of the time. It’s not happening. And if you believe like humans… it’s not happening. It’s got its own character.” This calls for new expectations and new oversight. 

Short summary: what to do tomorrow
• Use AI for speed: transcripts, summaries, desk research.
• Keep humans for depth: ethnography, cultural sensemaking, brand metaphors.
• Train your team on prompt engineering and on detecting AI hallucinatory patterns.
• Validate synthetic insights against small primary samples before scaling.

Final provocation

“AI is a genie, not a nemesis.” Use it to expand your reach, not to replace the human work that makes brands meaningful. The blind test proves the point: polish without provenance can mislead. Put systems, specs, and skepticism in place — and you will have a powerful co-pilot for brand insight.