Will AI replace Market Research Analysts? AI exposure 78/100

# Market Research Analysts

Market Research Analysts: very high exposure to AI (78/100), with change likely within 0–3 years. AI now designs surveys, codes open-ended responses, summarises transcripts and drafts reports, compressing weeks of analysis into days.

- Canonical: https://www.careerguard.ai/reports/market-research-analysts
- Markdown: https://www.careerguard.ai/reports/market-research-analysts/md
- PDF: https://www.careerguard.ai/reports/market-research-analysts/pdf
- Exposure: 78/100
- Window: 0-3 years
- Adoption: Very High Adoption
- Revised: 2026-10-05
- Free to read

## Overview

AI now designs surveys, codes open-ended responses, summarises transcripts and drafts reports, compressing weeks of analysis into days.

**Impact.** Survey platforms such as Qualtrics and SurveyMonkey generate questionnaires, check them for bias and theme thousands of open-text answers automatically. Qualitative work is changing too: Remesh and similar tools run large-scale moderated conversations and cluster the responses, while general models summarise interview transcripts and pull out quotes. The analyst's day shifts from cleaning data and writing first drafts to choosing the question, checking the machine's reading of the evidence and explaining what it means to a client who has to act on it.

**Risk.** Analysis and reporting are automating fast; human value moves to research design, interpretation and client counsel. Occupation-level measures place the role in the highest official exposure tier and observed usage of AI assistants is already high, which is consistent with how much of the work is text, tables and synthesis. Questionnaire drafting, open-end coding, cross-tab generation, chart production and first-draft reporting are all being automated in the current window. What remains human is deciding what is worth asking, judging whether the sample and method can bear the weight of the conclusion, catching the plausible but wrong AI summary, and persuading a client to change a decision. Employment in the broader occupation is projected to keep growing, so this is a role being reshaped at speed rather than removed; the analysts most at risk are those whose output is mainly the deck rather than the judgement behind it.

**Sector readiness.** Very High Adoption Across Research Platforms The major survey, panel and insight platforms have embedded generative features into the core workflow, so most agencies and in-house teams are already using AI whether or not they have a formal policy. Large research agencies and consumer-goods insight teams are furthest along, including experiments with synthetic respondents; public-sector and academic research moves more cautiously because of methodological and ethical scrutiny.

## Where you stand

Position yourself as the research designer who decides what to ask and which method will actually answer it, with AI handling the fielding and the first pass of analysis.

Become the analyst clients trust to challenge a machine-generated finding, because you understand sampling, bias and the limits of the data.

Move towards insight consulting, where the deliverable is a recommendation and a conversation with decision-makers rather than a report.

## What this means for you

- **Design is the job now.** Let the platform generate and code; make sure you are the person who frames the research question, chooses the method and defends the sample, because that is what the client is paying for.
- **Audit the themes.** AI-coded open-ends are fast but they merge and miss categories; read a sample of raw responses against every automated theme before it goes into a report.
- **Learn the limits of synthetic respondents.** Clients will ask about them; be able to explain where simulated answers are useful for pre-testing and where they are not a substitute for real people.
- **Present, do not just deliver.** A report that is read aloud by an AI summary has no author; a presentation where you answer hard questions does.
- **Pick up the quantitative tools.** Basic statistics, a bit of Python or R and comfort with data pipelines let you check and extend what the platforms produce.
- **Stay close to the decision.** Ask what the client will do differently as a result of the study, and write the research around that, because studies without a decision attached are the first to be automated away.

## Drivers of change

- **Generative features in survey platforms.** Qualtrics, SurveyMonkey and their rivals draft questionnaires, flag poor questions and summarise results inside the product, removing whole steps from the workflow.
- **Automated coding of open text.** Language models theme and sentiment-score thousands of verbatim answers in minutes, work that used to take a junior analyst days.
- **AI-moderated qualitative research.** Tools such as Remesh run large live conversations and cluster responses, blurring the line between qualitative depth and quantitative scale.
- **General-purpose assistants.** ChatGPT, Claude and Microsoft 365 Copilot summarise transcripts, draft reports and build charts, and observed usage in this occupation is already high.
- **Synthetic respondents.** Simulated panels built on language models are being tested for concept screening and pre-testing, reducing demand for some early-stage fieldwork.
- **Client pressure on cost and speed.** Buyers expect faster, cheaper studies now that the tools exist, which pushes agencies to automate everything that can be automated.

## Impact by sector

**Research agencies.** Agencies compete on price and speed, so the automation of fielding, coding and reporting is most advanced here and junior analyst intakes are shrinking.

**In-house consumer-goods and retail insight teams.** Teams embedded in the business are shifting towards continuous data and AI summarisation, with the analyst acting as internal consultant to brand and product teams.

**Healthcare and pharmaceutical research.** Regulatory and ethical requirements keep more human oversight in design and interpretation, slowing the pace of change.

**Public sector and social research.** Methodological transparency and sensitivity around populations mean AI is used for drafting and coding but rarely for unsupervised analysis.

## Skills to build

- **Research design and sampling.** Knowing which method answers which question, and what a sample can and cannot support, is the skill that AI output most depends on; formal training through a research association is worth the time.
- **Statistical literacy.** Being able to check significance, weighting and segmentation yourself lets you catch errors in automated analysis; a short applied statistics course covers most of what is needed.
- **Prompting and output verification.** Learn how to instruct AI tools precisely and how to test their summaries against the raw data, because unchecked AI output is the main new source of error in research.
- **Data handling in Python or R.** Light scripting lets you clean, merge and re-analyse data outside the platform's constraints and makes you useful to data science colleagues.
- **Storytelling and presentation.** Turning findings into a recommendation that an executive will act on is the part of the job furthest from automation; practise by presenting rather than sending.
- **Commercial understanding.** Understanding the client's business model and decision cycle lets you frame research around decisions, which is where the fee justifies itself.

## Tools in use

### Kinds of tool worth knowing

- **Synthetic respondent panels.** An emerging category of language-model-based simulated respondents used for concept pre-testing; understand the method and its limits before a client asks.

### Named tools

- **Qualtrics** ([https://www.qualtrics.com](https://www.qualtrics.com)). Experience-management platform whose AI features draft surveys, flag methodological problems and theme open-text responses.
- **SurveyMonkey Genius** ([https://www.surveymonkey.com](https://www.surveymonkey.com)). Built-in assistant that generates questionnaires, estimates completion rates and analyses responses within SurveyMonkey.
- **Remesh** ([https://www.remesh.ai](https://www.remesh.ai)). AI-moderated live conversation platform that clusters and summarises responses from hundreds of participants at once.
- **ChatGPT** ([https://chatgpt.com](https://chatgpt.com)). Used by analysts to summarise transcripts, draft discussion guides and generate first-pass report text.
- **Microsoft 365 Copilot** ([https://www.microsoft.com/microsoft-365/copilot](https://www.microsoft.com/microsoft-365/copilot)). Summarises research in Word and PowerPoint and helps build and interpret analyses in Excel.

## In practice

**Open-end coding at scale.** An analyst runs several thousand verbatim survey answers through the platform's AI theming, then checks a sample against each theme and merges or splits categories before reporting. Benefit: Qualitative depth is available on quantitative samples without a week of manual coding.

**Discussion guide and transcript synthesis.** A general assistant drafts a discussion guide from the research brief, then summarises each focus-group transcript with supporting quotes for the analyst to verify. Benefit: Turnaround on qualitative projects falls from weeks to days.

**Report first draft.** Copilot produces narrative text and charts from the cleaned data tables, and the analyst rewrites the interpretation and recommendation sections. Benefit: Analysts spend their time on what the findings mean rather than on formatting.

## How this role compares

**Copywriters** (More exposed). Generative models produce marketing copy directly, so the output of the role is substituted rather than assisted. Work moves to: Market research analysts keep a methodological role that copywriters lack; the lesson is to stay on the judgement side of the work.

**Data Scientists** (Different skills, growing). AI writes much of the code, but the demand for people who can build and validate models on business data keeps rising. Work moves to: Quantitative modelling and engineering skills; a realistic path for analysts who extend their statistics and scripting.

**Brand Managers** (Complementary, less exposed). Brand managers use AI for analysis and content, but ownership of strategy, budget and stakeholder decisions stays with the human. Work moves to: Commercial decision-making built on research insight; the natural client-side move for an experienced analyst.

## Closing judgement

The mechanics of market research, from fielding a survey to producing a deck of cross-tabs, are now largely machine work, and clients know it. Your value is in asking a better question than the client arrived with, knowing when the data cannot support the answer they want, and telling them so clearly. Get fluent with the AI features in your platforms so you can check their output rather than fear it, and spend the hours they free up getting closer to the business decisions your research is meant to inform.

## Evidence and revisions

**Revised 5 October 2026.** Score 78; window 0-3 years (unchanged).

Exposure Index v2. Inputs: task applicability 70/100 (Microsoft AI applicability score 0.35 for Market research analysts and marketing specialists); observed usage 86/100 (Anthropic observed exposure 0.65); official exposure tier 100/100 (BLS: very high); labour-market trajectory 32/100 (BLS projects employment to grow 7% over 2025–35); published adoption rating 85/100 (very high adoption). Weighted base 77.8. Final score 78. New report: the window of 0-3 years is set from the score band.

**How the figure is built (Exposure Index v2).**

| Input | Scaled (0–100) | Weight | Points |
| --- | ---: | ---: | ---: |
| Task applicability (Microsoft Research, AI applicability score) | 70 | 35% | 24.5 |
| Observed usage (Anthropic Economic Index, observed exposure) | 86 | 20% | 17.3 |
| Official exposure tier (US BLS AI-exposure category) | 100 | 20% | 20.0 |
| Labour-market trajectory (US BLS projected employment change 2025–35) | 33 | 10% | 3.3 |
| Published adoption rating (This report’s adoption level) | 85 | 15% | 12.8 |
| **Weighted base** | | | **77.8** |
| **Exposure score** | | | **78** |

### Measures behind the score

- **US Bureau of Labor Statistics, Employment Projections 2025–35 and AI Exposure Categories (27 August 2026).** AI-exposure tier: Very high. Projected employment change 2025–35: +7%. Matched to Market research analysts and marketing specialists. [publisher](https://www.bls.gov/news.release/ecopro.htm) · [PDF](https://www.bls.gov/news.release/pdf/ecopro.pdf) · [archived copy](https://vhjtvcmznlsxakprdimp.supabase.co/storage/v1/object/public/research-public/bls-employment-projections-2025-35.pdf) · [data](https://www.bls.gov/emp/ind-occ-matrix/ai-exposure-categories.xlsx)
- **Microsoft Research, Working with AI: Measuring the Applicability of Generative AI to Occupations (10 July 2025).** AI applicability score 0.35 for SOC 13-1161; scaled to 70/100 as the task-applicability input. [publisher](https://arxiv.org/abs/2507.07935) · [PDF](https://arxiv.org/pdf/2507.07935) · [archived copy](https://vhjtvcmznlsxakprdimp.supabase.co/storage/v1/object/public/research-public/microsoft-working-with-ai-2025.pdf) · [data](https://github.com/microsoft/working-with-ai)
- **Anthropic, Anthropic Economic Index report: Cadences (26 June 2026).** Observed exposure 0.65 for SOC 13-1161; scaled to 86/100 as the observed-usage input. [publisher](https://www.anthropic.com/research/economic-index-june-2026-report) · [PDF](https://cdn.sanity.io/files/4zrzovbb/website/9e0eadc8097864886c5d5060ebb1f89b02ea29d6.pdf) · [data](https://huggingface.co/datasets/Anthropic/EconomicIndex)
- **UK Department for Science, Innovation and Technology, Assessment of AI capabilities and the impact on the UK labour market (28 January 2026).** UK context: around 70% of UK workers are in occupations with tasks AI could perform or enhance, above the US average; a one-standard-deviation rise in exposure was associated with a 3.9% fall in UK job postings. [publisher](https://www.gov.uk/government/publications/assessment-of-ai-capabilities-and-the-impact-on-the-uk-labour-market/assessment-of-ai-capabilities-and-the-impact-on-the-uk-labour-market) · [archived copy](https://vhjtvcmznlsxakprdimp.supabase.co/storage/v1/object/public/research-public/dsit-uk-labour-market-assessment-2026.pdf)

Full research library, with licences and archived copies: https://www.careerguard.ai/sources

## Method and sources

Each report was written from a large body of published research. The exposure score itself is computed, not written: it is the CareerGuard Exposure Index, a weighted average of occupation-level measures from the US Bureau of Labor Statistics (AI-exposure classification and 2025–35 projections), Microsoft Research (AI applicability scores) and Anthropic (observed exposure), together with the adoption rating published on the report. The organisations and publications below are the standing literature behind the narrative sections. Every source, with dates, licences and archived copies where we are permitted to hold them, is catalogued in the research library.

Exposure Index v2 (October 2026). Each input is scaled to 0–100 and weighted: task applicability 35% (Microsoft AI applicability score ÷ 0.5), observed usage 20% (Anthropic observed exposure ÷ 0.75), official exposure tier 20% (BLS very high = 100, high = 70, moderate = 40, low = 10), labour-market trajectory 10% (50 − 2.5 × projected % employment change), published adoption rating 15% (very high = 85, high = 70, medium-high = 55, medium = 40, low-medium = 25, low = 10). Inputs not measured for an occupation are dropped and the remaining weights renormalised. An editorial adjustment of at most ±12 points is allowed only for automation channels the measures cannot see (robotics, self-service, machine vision, medical imaging, RPA/OCR, generative video) and is always logged with its reason. Scores are whole numbers, not rounded to five. The change window shifts one notch (a year at each end) per ten points of movement.

### Global and macroeconomic impact of AI on work

- **World Economic Forum:** The Future of Jobs Report series — Employer survey of expected job growth and decline, skill shifts and technology adoption (2020, 2023 and 2025 editions); Artificial Intelligence and the Future of Entry-Level Work (2026).; AI governance and transformation reports — Frameworks on ethical AI, talent strategy and industry transformation.
- **McKinsey Global Institute:** AI, Automation and the Future of Work series — Research quantifying automation potential by task, sector and demographic, from "Jobs Lost, Jobs Gained" to "Agents, robots, and us" (2025).; Industry-specific reports — Financial services, healthcare, manufacturing and others.
- **PwC:** Global AI Jobs Barometer — Annual analysis of job postings and productivity by AI exposure (2024–2026 editions).; Upskilling Hopes and Fears survey — Employee perceptions and readiness.
- **Microsoft Research and Anthropic:** Working with AI (2025); Anthropic Economic Index (2025–2026) — Occupation-level usage data from Copilot and Claude conversations, the two observed-usage measures behind the 2026 revision.
- **Stanford Digital Economy Lab and Stanford HAI:** Canaries in the Coal Mine? (2025–2026); AI Index Report (annual) — Payroll evidence on early-career employment in exposed occupations; annual measurement of AI capability, investment and adoption.
- **Deloitte:** Human Capital Trends series — Workforce, talent and HR technology trends.; Tech Trends series — Emerging technologies and their business implications.
- **Accenture:** Technology Vision series — Forward-looking analysis of emerging technology, with emphasis on AI.; Fjord Trends — Design, innovation and human experience in a digital world.
- **Boston Consulting Group:** AI/ML insights and industry solutions — "The AI Revolution in the Workplace" and related research.
- **EY:** AI and workforce reports — Adoption, talent strategy and ethics.
- **IBM Institute for Business Value:** AI and automation studies — Business models, workforce evolution and leadership.
- **OECD:** AI Policy Observatory — International data and policy on AI, labour markets and skills.; Employment Outlook — Labour-market trends including technological impact (2023–2026 editions).
- **International Labour Organization:** Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025) — Task-level exposure gradients for every ISCO occupation; successor to the 2023 global index.
- **International Monetary Fund:** Staff Discussion Notes on AI and work (2024, 2026) — Complementarity framing: where AI augments and where it substitutes.
- **UK Department for Science, Innovation and Technology:** Assessment of AI capabilities and the impact on the UK labour market (2026) — UK occupational exposure and job-posting evidence.
- **Brookings Institution:** AI and automation research — Economic and social implications, displacement and skills.
- **Yale Budget Lab and Goldman Sachs Research:** Tracking the Impact of AI on the Labor Market; AI and the US labour market (2026) — Aggregate labour-market monitoring; macro displacement estimates.
- **Oxford University (Oxford Martin School):** The Future of Employment — Frey & Osborne and subsequent research on susceptibility to automation.
- **MIT Technology Review:** AI & Work — Reporting on AI research and its implications for industries and jobs.
- **Gartner:** Hype Cycle for Artificial Intelligence — Maturity and adoption of AI technologies.; Future of Work reports — Workplace models and talent strategy.
- **U.S. Bureau of Labor Statistics:** Employment Projections 2025–35; AI Exposure Categories; Occupational Outlook Handbook — Ten-year employment projections and, from the 2025 cycle, an AI-exposure tier for every detailed occupation.
- **Indeed Hiring Lab:** AI at Work Report (2025) and posting-market updates — Skill-level transformation estimates and job-posting trends by occupation.

### Core AI and machine-learning research

- **OpenAI:** Research papers, blog and API documentation — Large language models, generative AI, safety and societal impact.
- **Google DeepMind:** Research papers and blog — Reinforcement learning, AI for science, AGI and ethics.
- **Meta AI:** Research papers and blog — Large language models, computer vision, AI for social good.
- **Hugging Face:** Transformers library and model hub — Open-source state-of-the-art NLP models.
- **TensorFlow and PyTorch:** Documentation and community forums — Core frameworks illustrating practical capability.
- **arXiv:** cs.AI, cs.LG, cs.CV, cs.CL — Pre-print research.
- **NeurIPS and ICML:** Conference proceedings — Top-tier academic research.
- **ACM and IEEE:** Journals and proceedings — ACM Computing Surveys; IEEE Transactions on AI.
- **Kaggle:** Datasets and competition solutions — Applied machine learning on real-world problems.
- **The Alan Turing Institute:** Research and reports — Responsible and applied AI.

### Ethical and responsible AI deployment

- **NIST:** AI Risk Management Framework — Voluntary framework for managing AI risk.
- **European Commission:** AI Act — Risk-tiered legal framework for AI.; Ethics Guidelines for Trustworthy AI — Principles for responsible development.
- **Partnership on AI:** Research and best practice — Responsible AI development.
- **AI Now Institute:** Annual reports — Social implications: power, inequality, rights.
- **ACM FAccT:** Proceedings — Fairness, accountability and transparency.
- **Data & Society:** Publications — Social implications of data-centric technology.
- **WIPO:** Conversation on IP and AI — Intellectual-property implications of AI.
- **IEEE Global Initiative on Ethics of A/IS:** Ethically Aligned Design — Recommendations for ethical AI design.
- **Center for AI and Digital Policy:** Policy briefs — Accountable AI policy.