June 9, 2026
Editor’s Note: AI agents can improve efficiency, scalability and consistency across research workflows, but human judgment remains essential for ensuring research quality, credibility and actionable insights. The future of AI-enabled market research depends on combining automation with expert oversight.
Market research has always depended on human judgment. AI agents are changing where that judgment matters most. Much of the industry is still debating whether AI agents matter. The real question is already different: how can organizations use AI agents effectively without automating away the very thing that makes market research, insights and decision-making credible?
AI agents are different from the AI tools the industry has used for years. Unlike traditional AI services and tools that generate content, summarize text or answer questions, AI agents can do more than generate outputs. They can plan, reason and execute workflows without waiting for step-by-step human instruction. That makes them genuinely useful for research, including transforming how research is designed, executed, analyzed and delivered. It also raises questions about where human judgment belongs and what happens when it is removed too early.
At Escalent, our position is simple: the future of market research and insights won’t be defined by AI alone. It will be defined by how effectively we combine AI capabilities with human expertise, methodological rigor, behavioral understanding and responsible governance.
Why AI Agent Automation in Market Research Is the Starting Point, Not the Goal
When people hear about AI agents, they tend to assume the goal is automation. We see it differently.
Market research requires both science and judgment, yet a lot of the work involves repetitive, process-heavy tasks that aren’t a great use of a researcher’s time.
Examples of research tasks where AI agents can improve efficiency include:
- Survey and questionnaire design
- Data quality and quota monitoring
- Data narratives and insights generation
- Pattern and anomaly identification
AI agents are often best at work researchers should spend the least time doing. The goal is not fewer researchers. The goal is fewer wasted researcher hours. What frees up is time for the work that actually requires a person—understanding behavior, synthesizing insights, making recommendations. The future is not human vs AI. It is humans spending more time where judgment actually matters.
“The greatest opportunity for AI agents is not replacing experts. It is removing low-value operational work so people can focus on interpretation, judgment and decisions that create business impact.”
Why Successful AI Agent Initiatives Start With the Research Problem, Not Technology
Every agent initiative at Escalent starts with a simple question: what problem are we trying to solve?
The temptation with emerging technology is to start with the tool and search for a use case. We intentionally work in the opposite direction. We begin by identifying research workflows where improvements in speed, consistency, quality or scalability can create meaningful value for clients and researchers alike.
Before we build anything, we define success upfront. This means:
- Clearly identifying the outcome we are trying to improve
- Establishing how research quality will be measured
- Determining where human review should remain an integral part of the workflow
- Assessing potential risks and implementing appropriate governance controls
The people shaping these solutions aren’t just technologists. They are researchers, behavioral scientists, operations specialists and subject matter experts who understand the nuances of research quality and methodology. In market research, the biggest AI failures are rarely technical failures. They are judgment failures.
“The organizations that benefit most from AI agents are not those that automate the most. They are the ones that combine AI capabilities with human expertise, governance, and clear business objectives.”
What Can AI Agents Do Well in Market Research—and Where Do They Fall Short?
A few things have surprised us along the way.
One of the biggest and hardest lessons we have learned is that the smartest agent in the world is only as good as the knowledge it can access. Agents require access to trusted information, historical context, methodological guidance and business-specific knowledge. Organizing and governing that information has consistently taken more effort than building the technology itself.
Research looks structured from the outside. In practice, it is full of exceptions. Different methodologies, client requirements, market contexts and study designs create situations that cannot always be anticipated upfront. A solution that performs exceptionally well in most scenarios still needs safeguards for edge cases and unexpected conditions.
Most AI adoption problems are not technology problems. They are trust problems. New ways of working require trust and trust requires transparency, achieved by a genuine understanding of not just what AI is doing, but why. The investment in education and change management matters as much as the underlying technology.
Finally, agents also have real capability limits that are worth naming. They handle well-structured, repeatable tasks reliably. They struggle with novel methodologies, cross-cultural nuances, and situations where the data tells two conflicting stories. Knowing where those limits are is part of using them responsibly.
Why Trust Has Become the Most Important Product in AI-Enabled Research
One principle guides everything we do here: transparency.
At Escalent, we believe AI adoption isn’t something that happens to our clients. We navigate it together. As AI agents become more embedded in research, insights and decision-making workflows, clients have legitimate questions about how outputs are generated, where human review applies, how quality is validated and what happens when something goes wrong. These are not obstacles to adoption but are the right questions that deserve clear and transparent answers. Answering them openly is how trust gets built.
Our job is to make sure clients understand not just the benefits of AI-assisted workflows, but the governance and oversight that support them. In practice, that means documenting which parts of a workflow involved AI, flagging outputs that went through automated generation versus human synthesis and building in explicit review checkpoints before anything client-facing goes out the door.
That said, AI makes it dangerously easy to confuse faster with better. Clients often want both and AI makes it tempting to promise both without thinking carefully about where corners are being cut. Our job is to be honest about those tradeoffs, not dress them up as features.
The goal is not simply to make research faster. The goal is to make research better.
The Future of AI-Powered Research Will Belong to Organizations That Know What Not to Automate
AI agents will become a foundational component of the market research ecosystem. As capabilities mature, agents will increasingly collaborate across research workflows, helping teams navigate larger datasets, more complex methodologies and growing demands for speed and responsiveness.
At Escalent, we are preparing for that future by:
- Investing in production-ready AI capabilities grounded in real research workflows
- Building our teams’ skills so researchers can work effectively with AI rather than around it
- Partnering with clients to build responsible AI governance frameworks
- Preserving trust, transparency and research integrity throughout adoption
The winners in AI-powered research will not be the firms that automate the most. They will be the firms that know what should never be automated in the first place.
Key Questions
Q: How can organizations adopt AI agents without sacrificing research quality?
Organizations should begin with clearly defined business and research objectives rather than implementing AI for its own sake. Successful adoption requires identifying workflows where AI can improve efficiency, consistency or scalability while maintaining appropriate human oversight. Research quality is best protected when organizations establish clear success metrics, apply governance controls, document how AI is used and retain human judgment for activities that require contextual understanding, methodological expertise and strategic decision-making. The goal is not simply to do research faster, but to make it better while preserving trust, rigor and integrity.
Q: What are the biggest limitations of AI agents?
AI agents perform best in structured, repeatable workflows where processes and rules are well defined. They are less effective when work requires novel methodologies, cross-cultural nuance, conflicting evidence, contextual understanding or complex judgment. Understanding these limitations is essential for using AI responsibly and effectively.
Q: Why is human oversight still important when using AI agents?
Human oversight helps ensure quality, identify errors, apply contextual judgement and maintain accountability. While AI agents can improve efficiency and scalability, experts remain responsible for interpreting findings, validating outputs and determining how insights should be applied. The most effective AI-enabled workflows combine automation with meaningful human review.
Q: Why do AI agents require trusted knowledge and governance?
AI agents are only as effective as the information, context and guidance available to them. To produce reliable outputs, they need access to trusted knowledge, historical context, methodological frameworks, and organization-specific information. Governance plays a critical role in ensuring that data sources are accurate, processes are transparent and appropriate safeguards are in place. Without strong knowledge management and governance, even highly capable AI agents can generate inconsistent, incomplete or misleading results. Responsible AI adoption requires both quality information and clear oversight to maintain trust, accuracy and research integrity.
Want to learn more? Let’s connect.
Abhinav Kothari
Chief Information & Technology Officer
Abhinav Kothari is Chief Information & Technology Officer. He's a seasoned technologist driven by a deep passion for leveraging technology to augment human capabilities and known for his collaborative approach to delivering software solutions that add significant value to end-users. His expertise in automation and artificial intelligence, including intelligent automation and actionable intelligence, is pivotal in advancing Escalent’s digital transformation initiatives. Before joining Escalent, Kothari was the Chief Technology Officer at Vivvix. In this advertising intelligence company, he introduced a state-of-the-art technology stack and led the creation of a data office to enhance data transparency and accessibility. Prior to Vivvix, he served as vice president of engineering at MRI-Simmons, where he integrated platforms and spearheaded digital conversions during the COVID-19 pandemic. Kothari earned a degree in computer science engineering from University of Pune, India.
