Generative AI and the Reinvention of Market Research: From Speed to Strategic Insight

Generative AI has moved from experimentation to enterprise adoption in just a matter of months. For market researchers, this is not just a new analysis tool; it changes the workflow, speed, and types of insights we can reliably deliver. Organisations report dramatic increases in AI adoption across business functions, and marketing and insights teams are among the most disrupted by generative models.

1. Routine work is automated; human strategists scale higher-value work

Tasks such as transcription, initial coding of open-ended questions, template reporting, and rapid segmentation prototypes are now commonly automated, freeing researchers to design better studies and focus on interpretation and stakeholder storytelling. This shift is reflected in major surveys, which show rapid year-on-year increases in organisational AI usage.

2. New signal types and faster hypothesis testing

Generative models enable quick synthetic testing (e.g., simulated consumer personas, automated sentiment extrapolation), allowing researchers to iterate on hypotheses faster before committing to expensive fieldwork. Early adopter firms have integrated gen-AI into pilot phases to reduce time-to-insight.

3. End-to-end workflow redesign and governance

To capture value, firms are redesigning workflows (not just dropping LLMs into old processes). Governance, encompassing model selection and prompt design, as well as audit trails for outputs, is now an essential capability. Consulting and research leaders recommend establishing AI governance and senior oversight to align outputs with business risk tolerance.

Speed: Rapid synthesis of qualitative data and quicker reporting cycles (days -> hours for many deliverables).
Scale: Ability to analyse far larger text/video/voice datasets without linear increases in cost.
Consistency: Algorithmic coding reduces coder variance on repetitive tasks, while humans validate higher-level themes.

Hallucinations/factual errors: LLMs can invent plausible-sounding but false facts; human validation remains essential.
Bias amplification: Historical biases in training data can manifest in segmentation, persona building, or sentiment outputs; design bias checks and regular audits are necessary.
Data privacy and compliance: Use of sensitive respondent data in third-party models can create regulatory exposure. Build privacy-preserving flows and keep raw personal data off general LLM services.

1. Discovery + Pilot (2-4 weeks)

Identify 1-2 high-volume repetitive tasks (e.g., open-end coding, transcript summaries). Prototype with internal data, measure error vs. human baseline.

2. Hybrid workflow

Automate first-pass coding and summarisation; route outputs to human analysts for validation and theme synthesis.

3. Prompt engineering library

Capture high-quality prompts and evaluation rubrics as reusable assets.

4. Governance and audit logs

Track model versions, prompts, inputs and outputs for each deliverable. Keep human-in-the-loop sign-offs for client deliverables.

5. Privacy-first deployment

Use hosted/private models when processing personal or sensitive respondent data, and anonymise it before making external model calls.

Large-scale qualitative synthesis: Convert hundreds of interview transcripts into structured themes and prioritised recommendations in a fraction of the usual time.
Rapid concept optimisation: Generate multiple concept wording variants and simulated consumer reactions for faster A/B testing design.
Executive-ready dashboards and narratives: Auto-generate first-draft storylines for dashboards, with human editors finalising strategic recommendations.
Measurement: how to know it’s working
Track these KPIs before and after AI integration:

• Time-to-first-insight (hours/days)
• Per centt of deliverable automated (and % human validation)
• Client satisfaction / Net Promoter Score for insight products
• Error rate (AI vs. human) on a representative sample

• Ask vendors about model provenance, data handling, and human validation processes.
• Favour partners that provide explainability (how a theme was derived) and audit trails for outputs.
• Pilot in low-risk areas first, scale to high-stakes deliverables only after governance is in place.

Generative AI isn’t a magic wand, but when embedded into disciplined workflows and governed responsibly, it multiplies the reach of skilled researchers and shortens the path from data to decision. At Eklavya, we design hybrid workflows that combine AI speed with analyst judgment to deliver reliable, actionable insights your business can act on.

Want a tailored AI pilot for your insights team? Contact Eklavya for a 2-week feasibility pilot that benchmarks AI-assisted coding vs. human coding and maps the ROI.