How AI Agents Are Changing the Researcher’s Role and Highlighting What Only Humans Can Do
In a profession built on asking the right questions, marketing researchers now face one of their own: How will we adapt as AI becomes a true collaborator in our work? As AI agents evolve, we are approaching a time when portions of the research workflow, particularly the routine and time-consuming tasks, will increasingly be carried out with little or no human involvement. Current tools are mostly semi-autonomous, but true autonomy is on the horizon. This transformation is not just about speed or cost. It challenges us to reconsider the value we bring and to focus our energy where human insight matters most, such as planning, interpretation, strategy, and turning insights into action.
What Are AI Agents
An AI agent is a software program that uses artificial intelligence technologies to semi-autonomously or autonomously perform tasks, solve problems, and make decisions. These agents can operate in various environments, including digital platforms like customer service chatbots, physical environments such as autonomous vehicles, hybrid settings like smart factories that integrate sensors and robotics, and research-specific systems that manage data collection and analysis. They are built to adapt to new inputs over time and often incorporate machine learning, natural language processing, computer vision, which enables agents to interpret visual data such as images or video, and multi-agent orchestration, where multiple specialized agents collaborate in a coordinated sequence to complete complex tasks efficiently.
AI agents are already a big part of our lives. They include semi-autonomous virtual assistants like Siri and Alexa as well as more advanced systems like autonomous vehicles that processes vast sensor data to make decisions in real time. In the context of research, AI agents are beginning to operate with increasing autonomy, handling tasks such as survey fielding and insight summarization.
How Do AI Agents Differ From Traditional AI
The key word here is autonomy. While traditional AI may include models or algorithms that require direction, configuration, or human-initiated queries, AI agents are decision-makers. For example, ChatGPT is a traditional AI tool. It generates responses based on user prompts but does not initiate actions or make decisions independently. In contrast, AI agents act on their environment, learn from it, and adapt without needing step-by-step instructions.
Unlike large language models that generate text or predictive algorithms that surface correlations, AI agents can sequence and complete entire tasks. For example, an AI agent can receive a goal such as "conduct a key driver analysis on brand affinity for Nike across TikTok, Instagram, and Reddit," identify the relevant data sources, retrieve and clean the data, determine which factors most influence consumer sentiment or engagement, and summarize the findings in a visual report without human input beyond setting the initial goal.
Tasks AI Agents Are Likely to Fulfill in Marketing Research
AI agents are already beginning to reshape the marketing research process, and their impact is likely to grow. They are being applied across both quantitative and qualitative workflows, with capabilities extending into areas that were once considered exclusively human domains.
Study Setup and Fielding
Agents can design basic to moderately complex survey instruments, launch studies across multiple platforms, manage quotas, and monitor sample composition in real time. They can adjust on the fly if certain demographic groups are underrepresented or if drop-off rates suggest a need for question redesign. For more sophisticated questionnaires, human input may still be required to ensure precision in logic, flow, and methodology. In qualitative research, Agents can also manage participant scheduling, screen for qualified respondents, and moderate simple asynchronous discussions.
Data Cleaning and Preparation
Rather than rely on static scripts or manual oversight, AI agents can automatically and continuously detect outliers, remove duplicates, flag inconsistent responses, and reformat data in preparation for analysis. In qualitative contexts, they can transcribe audio, remove filler words, anonymize participant data, and segment transcripts by theme or speaker.
Analysis and Interpretation
Advanced agents can go beyond descriptive statistics. They can run multivariate models, perform key driver analysis, and compare findings across time periods or segments. With access to external data sources, they can enrich internal datasets to provide broader context and deeper insights. In qualitative research, they can analyze open-ended responses, identify recurring patterns, and extract sentiment and emotion from text or voice data.
Summary Reporting
AI agents can generate reports, craft narrative summaries, and create executive-ready data visualizations. Some are even capable of tailoring the output based on the audience, such as marketing, product, or executive leadership, by adjusting the level of detail and framing accordingly. In qualitative work, they can generate topline summaries, highlight representative quotes, and visualize emergent themes. However, AI has limitations. It may overlook subtle cues, cultural context, or strategic implications. Human interpretation is often required to validate findings, ensure accuracy, and align insights with broader business objectives.
Continuous Learning and Iteration
Agents can improve their performance over time by learning from outcomes. This approach is known as reinforcement learning. It means that based on what works and what does not, they can identify which types of questions yield the most actionable insights, which audiences engage best, and what types of stimuli perform well. They can apply what they learn to refine future research designs automatically. However, human researchers play an essential role in guiding this learning process. They validate outputs, provide feedback loops, and help AI agents adapt to evolving business priorities, social contexts, and client expectations. This collaborative dynamic ensures that AI continues to improve in ways that remain strategically relevant and aligned with real-world needs.
Current AI agents in marketing research are semi-autonomous, capable of completing specific tasks with minimal human input but still requiring oversight and direction. Fully autonomous agents, which can manage entire research workflows independently, are still emerging. Even so, several tools already demonstrate agent-like capabilities. Platforms such as unSurvey.ai can conduct voice-to-voice interviews with autonomous probing and summarization. Tools like Yabble and Recollective AI help automate key steps such as data cleaning, theme extraction, and report generation. While human validation and strategic interpretation remain essential, these technologies represent meaningful progress toward more automated and efficient research processes.
The Role of Human Researchers in an Agent-Driven Workflow
As AI agents take on more of the marketing research workload, the role of human researchers must evolve. Far from being obsolete, human expertise is becoming even more essential.
Researchers will increasingly serve as strategists, curators, and interpreters. Our focus will shift toward framing the right business questions, setting research priorities, validating AI-generated findings, incorporating human insight, and translating results into actionable recommendations. Human oversight remains critical for ensuring ethical integrity, contextual relevance, emotional nuance, and alignment with broader business objectives.
In complex or ambiguous situations, human judgment is indispensable. It is needed to recognize subtle cues, determine what truly matters, and make decisions that reflect emotional intelligence, cultural awareness, and brand sensitivity. These are areas where AI still falls short.
Implications for Speed, Accuracy, and Oversight
The use of semi-autonomous and autonomous AI agents in research brings several implications.
Time Savings
Research cycles that once took weeks can now be completed in hours or even minutes. This enables faster iteration, shorter decision cycles, and greater agility.
Accuracy and Consistency
With agents applying standardized processes and learning from prior work, output becomes more consistent, reliable, and reproducible.
Oversight and Trust
As automation expands, trust must be earned. Researchers must validate outputs, guard against data hallucinations, and ensure that decisions remain aligned with business intent.
From Agents to Analysts: A Collaborative Future
The research function is evolving. AI agents are beginning to take on more of the routine workload, opening new possibilities for faster, more adaptive, and scalable research. This shift allows researchers to focus on the work that truly requires human intelligence, such as defining the right problems, interpreting findings in context, and turning insights into strategic action.
Rather than replacing researchers, AI is becoming a collaborator. It handles the routine while people provide the perspective. As this dynamic continues to take shape, success will not be measured by the volume of tasks completed but by the relevance, clarity, and impact of the insights delivered.
For marketing researchers, this evolution presents both a challenge and an opportunity. It may call for new tools, new thinking, and new approaches. But it also offers the chance to deepen your value and expand your influence. Those who are willing to learn, adapt, and partner with AI will be well positioned to help shape the future of our field.
Retraining for the AI-Driven Future
How will we adapt? Researchers do not need to become data scientists. However, they do need to understand how AI works and where it fits. Start by exploring AI tools relevant to your work, such as platforms for text analysis, data visualization, or voice-based insights. Learn the basics of AI through curated courses or webinars, and practice using these tools on small projects. Most importantly, focus on strengthening the skills that AI cannot replicate. Strategic thinking, contextual interpretation, and insight storytelling will remain central as the research landscape continues to evolve.
Kirsty Nunez is the President and Chief Research Strategist at Q2 Insights, a research and innovation consulting firm with international reach and offices in San Diego. Q2 Insights specializes in a wide range of research methodologies and predictive analytics. The firm uses AI tools to enhance the speed and quality of insights delivery while relying on the expertise and judgment of human researchers. AI is applied exclusively to respondent data and is never used to generate findings, which remain grounded in human analysis and interpretation.
