Disclaimer: This article is for general information and industry commentary only and does not constitute legal, regulatory, financial or professional advice. Organisations using artificial intelligence, personal data or automated decision-making systems should obtain appropriate professional advice and ensure compliance with applicable data protection, research and industry standards.
Could agentic AI transform how businesses design studies, analyse markets and make decisions, and where does the human researcher fit?
Artificial intelligence has already changed market research.
Researchers use generative AI to draft questionnaires, summarise interviews, analyse open-ended responses, clean data, identify themes and assist with reporting.
But the next stage may be considerably more significant.
Instead of simply giving an AI tool an instruction and waiting for a response, businesses are beginning to explore AI agents capable of planning and carrying out a series of research tasks with much greater autonomy.
This is often described as agentic AI.
The distinction matters.
A conventional generative AI system might answer:
“What themes are appearing in these customer comments?”
An AI agent could potentially be given a much broader objective:
“Find out why customer satisfaction has fallen, identify the most affected customer groups, analyse existing research, locate gaps in our knowledge, recommend an appropriate methodology and prepare decision-ready findings.”
It could then work through multiple stages of that task, accessing approved data sources, choosing tools, performing analyses and reporting its findings.
That moves artificial intelligence from being merely a research assistant towards becoming part of the research infrastructure itself.
And the market research industry is already preparing for that transition.
As of 22 September 2026, the UK Market Research Society lists a dedicated course for 23 September entitled “AI Agents in Market Research: Revolutionising Data Insights and Decision-Making”, specifically examining autonomous AI agents across quantitative, qualitative and analytics workflows.
The question is therefore no longer whether AI will be used in market research.
It is becoming:
How much of the research process should AI be allowed to conduct autonomously?
What Is an AI Agent?
An AI agent is a software system designed not merely to generate content but to work towards an objective.
Depending on the system and the permissions it has been given, an agent may be able to:
- Interpret a business question;
- Break it into individual tasks;
- Select appropriate tools;
- Search approved information sources;
- Interrogate databases;
- Analyse datasets;
- Compare historical research;
- Identify missing evidence;
- Draft research instruments;
- Monitor competitors;
- Analyse qualitative responses;
- Generate visualisations;
- Produce summaries;
- Make recommendations; and
- Initiate further stages of a workflow.
This is where agentic AI differs from the familiar chatbot model.
The user does not necessarily have to instruct the system at every stage.
Instead, the agent can potentially decide what steps need to happen next within predefined boundaries.
From Research Cycles to Continuous Intelligence
Traditional market research frequently operates in projects.
A business identifies a question.
A brief is prepared.
An agency or internal research team designs the study.
Fieldwork takes place.
The data is cleaned and analysed.
A report is produced.
Stakeholders review the findings.
By the time the process is complete, several weeks may have passed.
Qualtrics highlighted this problem in May 2026, arguing that businesses are increasingly making decisions faster than conventional research cycles can produce answers. It said that more than 95% of market research professionals were already using AI for discrete tasks such as drafting questions, summarising findings and cleaning data.
The more profound development, however, is the transition from individual AI-assisted tasks towards systems capable of orchestrating the research lifecycle.
Qualtrics has described a model in which an agent could take a business question, develop a research plan, assist with methodology, field research, analyse findings and draw upon an organisation’s accumulated research library.
That could fundamentally change the role of the insight department.
Instead of producing research only when somebody commissions a project, an organisation could eventually maintain an always-on intelligence capability.
What Could an AI Research Agent Actually Do?
Imagine a retailer notices that sales of one particular product category have declined.
Rather than immediately commissioning a new survey, an AI research agent could potentially examine:
- Historical sales data;
- Customer satisfaction research;
- Previous surveys;
- Competitor activity;
- Pricing information;
- Product reviews;
- CRM data;
- Social-listening information;
- Customer service transcripts;
- Macroeconomic indicators; and
- Previously commissioned market reports.
The agent might identify that the organisation already possesses enough information to explain part of the problem.
It could then flag what is missing.
For example:
“Existing evidence suggests price sensitivity has increased among customers aged 25–34, but there is insufficient evidence regarding competitor switching behaviour. A targeted study is recommended.”
The agent could then assist with designing that study.
This is considerably different from asking ChatGPT to summarise a spreadsheet.
It is a movement towards AI-directed research workflows.
AI Agents and Quantitative Research
Quantitative research may be particularly suited to certain agentic applications.
An agent could potentially assist with:
- Questionnaire development;
- Sample requirements;
- Survey logic;
- Data cleaning;
- Weighting;
- Segmentation;
- Cross-tabulation;
- Statistical testing;
- Conjoint analysis;
- MaxDiff studies;
- Trend detection; and
- Dashboard monitoring.
Qualtrics has suggested that agentic systems could embed specialist methodologies into research workflows so that sophisticated approaches become more accessible while researchers retain oversight.
That accessibility could be valuable.
But it also introduces risk.
A powerful statistical technique does not become appropriate merely because software can perform it.
The research question, sample quality, assumptions and interpretation still matter.
AI Agents and Qualitative Research
Qualitative research raises a different set of opportunities and challenges.
AI systems can already summarise transcripts and group comments into themes.
Agentic systems may go further by:
- Comparing interviews across different audience groups;
- Highlighting contradictory responses;
- Identifying themes that deserve further investigation;
- Examining previous qualitative studies;
- Creating discussion-guide drafts;
- Recommending follow-up questions;
- Analysing sentiment and language; and
- Tracking emerging themes over time.
The danger is that qualitative research depends heavily on context.
A pause, tone of voice, cultural reference, contradiction or emotionally significant phrase may matter enormously.
Human researchers do not simply count words.
They interpret meaning.
That distinction becomes increasingly important as automation moves deeper into the research process.
AI Agents Could Become Competitor-Intelligence Researchers
Agentic AI could also change competitive intelligence.
Businesses traditionally monitor competitors through periodic reports, manual searches, alerts and analyst research.
An AI agent could continuously monitor authorised public sources for:
- Product launches;
- Pricing changes;
- Advertising campaigns;
- Executive announcements;
- Recruitment activity;
- Customer reviews;
- Patents;
- Regulatory filings;
- Press releases;
- Website changes; and
- Market sentiment.
The agent could then identify material changes rather than forcing a researcher to sift continuously through large volumes of information.
Instead of asking:
“What happened in the market last quarter?”
Decision-makers may increasingly ask:
“What has changed since yesterday that we need to know about?”
The Rise of the AI Consumer Agent
There is another dimension that market researchers cannot ignore.
AI agents are not only going to work for researchers and businesses.
They are increasingly working for consumers.
Kantar’s 2026 Marketing Trends report identifies AI agents as a major emerging force, describing a world in which consumers increasingly delegate product discovery and purchase decisions to AI systems. Kantar reports that 24% of AI users were already using an AI shopping assistant in the research it cites.
This creates an entirely new research problem.
Historically, brands have tried to understand:
What makes a human choose Brand A rather than Brand B?
Increasingly, researchers may also need to understand:
What makes an AI agent recommend Brand A rather than Brand B?
That could create a new field of research involving machine-mediated consumer behaviour.
Brands may need to understand not only human attitudes, emotions and preferences but also the information environments from which consumer agents make recommendations.
Researching Humans Who Delegate Decisions to Machines
This shift could alter consumer behaviour itself.
Suppose a consumer tells an AI agent:
“Find me the best value washing machine with good reliability, low energy use and delivery this week.”
The consumer may never visit ten retailer websites.
They may never see most advertising campaigns.
Their agent might compare specifications, reviews, pricing and delivery options before presenting three choices.
Traditional customer journeys could therefore change.
Market researchers may need to study questions such as:
- How much do consumers trust AI recommendations?
- Which decisions are people willing to delegate?
- When do consumers override their AI agent?
- What factors does the agent prioritise?
- Does brand loyalty remain important?
- Do consumers become more price-sensitive when comparison becomes automated?
- Will AI agents strengthen major brands or help lesser-known competitors?
- How does sponsored content influence agent recommendations?
- How should disclosure operate when an agent receives commercial incentives?
This could become one of the most significant developments in consumer research since the rise of online shopping.
What Happens to the Market Researcher?
Predictions that AI will simply replace researchers may misunderstand what high-quality research actually involves.
The repetitive components of the profession are certainly becoming increasingly automated.
But research is not merely data processing.
A skilled researcher asks:
- Is this the right question?
- Are we measuring what we think we are measuring?
- Is this sample representative?
- What assumptions are built into this model?
- What evidence contradicts our conclusion?
- Could bias explain the result?
- What information is missing?
- Is correlation being mistaken for causation?
- Does the finding make sense within the wider market?
- Can this result safely support a business decision?
Those are judgement questions.
Qualtrics argues that the researcher’s role may shift towards becoming a governor or architect of the research system, establishing standards and deciding what evidence should be trusted rather than manually carrying out every stage of execution.
The Market Research Society similarly states that it does not regard the future as a choice between AI and human expertise, but as a combination of both.
The Danger of Confidently Wrong Research
One of the greatest risks of agentic research is scale.
A human researcher can make a mistake.
An autonomous system can potentially make the same mistake thousands of times at extraordinary speed.
If an AI agent misunderstands a business question, selects an inappropriate methodology or relies on poor-quality information, it may generate an impressive-looking answer that is fundamentally wrong.
The presentation may still look professional.
The charts may still look convincing.
The language may still sound authoritative.
That creates what may become one of the defining research risks of the AI era:
false confidence at scale.
Qualtrics itself warns that an AI research system must know when evidence is missing rather than filling gaps with plausible-sounding but unsupported outputs.
Data Quality Becomes Even More Important
AI cannot rescue poor data.
It can analyse poor data faster.
If research databases contain:
- Fraudulent respondents;
- Outdated records;
- Duplicated participants;
- Unrepresentative samples;
- Badly worded survey questions;
- Biased historical research; or
- Incorrectly coded information,
an autonomous agent may propagate those weaknesses throughout subsequent analyses.
Data provenance therefore becomes crucial.
Researchers need to know:
- Where did this information come from?
- When was it collected?
- Who was represented?
- How reliable was the methodology?
- Has the AI changed or inferred anything?
Without that audit trail, businesses risk confusing generated intelligence with verified evidence.
Privacy, Personal Data and Agentic AI
AI agents may require access to large quantities of information to function effectively.
That creates significant privacy responsibilities.
In the UK, where AI systems process personal information, organisations still need to consider data-protection requirements. The Information Commissioner’s Office states that AI systems processing personal data fall within the data-protection framework and emphasises principles including lawfulness, fairness, transparency, purpose limitation, data minimisation, accuracy, security and accountability.
This becomes particularly important where agents are connected to:
- Customer databases;
- Survey responses;
- CRM systems;
- Employee information;
- Transactional records;
- Behavioural data;
- Health information; or
- Other sensitive datasets.
The fact that an AI agent can access a dataset does not necessarily mean it should.
Permissions, access controls and data minimisation will become central components of responsible agentic research.
Interestingly, the ICO currently lists specific agentic AI guidance as being drafted, with consultation expected in September 2026 and final guidance scheduled for spring 2027.
That alone demonstrates how quickly this technology is moving into mainstream regulatory consideration.
Market Research Ethics Still Apply
The arrival of AI agents does not remove traditional research responsibilities.
Researchers still need to consider:
- Informed participation;
- Privacy;
- Transparency;
- Confidentiality;
- Fairness;
- Data quality;
- Appropriate methodology;
- Protection of participants; and
- Responsible interpretation.
The Market Research Society has published specific guidance on using AI and related technologies and stresses that practitioners need to consider ethical principles carefully when developing or applying these systems.
Research automation should therefore not become an excuse for lowering professional standards.
If anything, greater automation requires stronger governance because fewer humans may be manually reviewing each stage of the process.
Human-in-the-Loop Research
One of the most sensible models for agentic research is likely to involve human-in-the-loop oversight.
Under this model, AI may perform substantial amounts of work while important decisions remain subject to human review.
For example, an agent might:
- Analyse the business problem;
- Propose a methodology;
- Identify existing evidence;
- Design a draft questionnaire;
- Recommend a sample;
- Analyse results; and
- Draft conclusions.
But a qualified researcher could still approve the methodology, validate the outputs and determine whether the conclusions are suitable for decision-making.
This preserves speed without surrendering accountability.
Could AI Agents Make Market Research More Accessible?
There is also considerable potential for smaller organisations.
Historically, sophisticated research capabilities have often required sizeable budgets, specialist teams or external agencies.
Agentic systems could make certain research capabilities accessible to:
- SMEs;
- Charities;
- Start-ups;
- Universities;
- Independent consultants;
- Non-profit organisations; and
- Smaller marketing departments.
A small company might eventually have access to analytical capabilities that previously required an entire research department.
That democratisation could stimulate competition.
However, access to powerful research technology must not be confused with mastery of research methodology.
Making research easier to perform does not automatically make the research good.
From Researchers to Research Architects
The profession itself may therefore evolve.
Tomorrow’s researchers may spend less time manually moving information between spreadsheets and presentations.
Instead, they may increasingly:
- Design AI research workflows;
- Govern agent permissions;
- Validate evidence;
- Audit data provenance;
- Supervise methodologies;
- Detect bias;
- Interpret nuance;
- Challenge AI-generated conclusions;
- Combine multiple forms of evidence; and
- Translate intelligence into commercial strategy.
That may actually increase the importance of experienced researchers.
When machines can generate hundreds of conclusions instantly, somebody still needs to determine which conclusions deserve attention.
The Competitive Advantage Will Not Be Having AI
Soon, almost every major research organisation will have access to advanced AI.
The competitive advantage therefore cannot simply be:
“We use artificial intelligence.”
That will become commonplace.
The real differentiators may instead be:
- Proprietary high-quality data;
- Trustworthy methodologies;
- Transparency;
- Expert human oversight;
- Sector knowledge;
- Ethical governance;
- Accurate validation;
- Good research design; and
- The ability to turn evidence into sound decisions.
AI may commoditise some research tasks.
It may simultaneously increase the value of judgement.
Conclusion
AI agents represent a significant evolution in market research.
Generative AI helped researchers work faster.
Agentic AI could go considerably further by coordinating research itself.
It may design studies, interrogate existing evidence, analyse markets, monitor competitors, identify gaps and generate decision-ready intelligence.
At the same time, consumers themselves are beginning to use AI agents to research products and make purchasing decisions, potentially creating an entirely new layer of machine-mediated consumer behaviour.
The opportunity is enormous.
So are the responsibilities.
Organisations adopting AI agents will need to think carefully about methodology, data quality, privacy, transparency, security and human oversight.
The future of market research is therefore unlikely to involve researchers being replaced by autonomous machines.
A more plausible transformation is one in which the machines handle more of the process while researchers become responsible for something even more valuable:
Deciding what should be asked, what should be trusted, and what businesses should do with the answers.
AI agents may become extraordinarily capable researchers.
But capability alone is not judgement.
And in a world where intelligence can increasingly be generated automatically, trusted human judgement may become the most valuable insight of all.
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