Artificial intelligence (AI) is transforming countless industries, and market research is no exception. Yet, despite its promise, many organizations struggle with implementation. The prevailing approach often positions AI as a replacement for human expertise – autonomous systems that aim to fully automate complex workflows. However, our experience suggests a different approach: the most effective AI solutions enhance and amplify skilled human operators rather than replace them.
This approach reflects a broader shift in the relationship between research firms and their technology partners. As an increasing portion of market research workflows becomes automated, the most successful approaches move beyond the traditional vendor-client relationship toward true partnership. In this model, human expertise guides the development and refinement of AI systems, while AI handles the repetitive tasks that previously consumed researchers' valuable time.
The partnership between The Directions Group and Roundtable exemplifies this philosophy. As one of the first research firms to adopt Roundtable's AI-powered technology for survey data quality, The Directions Group pioneered a new model of human-in-the-loop development.
This report examines how this approach to AI is revolutionizing data quality in market research, using the Roundtable and The Directions Group partnership as a case study. We'll explore the specific challenges this approach addresses, the technical and organizational factors that make it effective, and the broader implications for the future of market research.
Maintaining high-quality survey data has long been a central challenge in market research. The Directions Group employs advanced digital fingerprinting, intentional sample composition, and survey data checks as part of a comprehensive and evolving approach to data quality. This report focuses specifically on open-ended survey responses, where quality control has traditionally involved researchers manually reviewing each response to identify potential issues. While effective, manual review faced three critical limitations: it was slow, struggled to scale to handle growing response volumes, and placed a significant cognitive burden on researchers.
AI excels at automating repetitive tasks and detecting subtle patterns that escape human notice. However, the key to effectively applying AI to data quality isn't to completely automate the decision-making process, but rather to augment human expertise. Roundtable's approach exemplifies this philosophy by providing a comprehensive set of flags and indicators that help researchers focus their attention where it's most needed. After evaluating numerous competing solutions in the market, The Directions Group found Roundtable's technology consistently outperformed alternatives.
Roundtable's system analyzes open-ended responses through three complementary lenses, each targeting specific threats to data quality.
First, survey participants often submit low-effort responses that offer little valuable information or insight. Roundtable’s content analysis addresses this by using large language models to determine whether responses meaningfully address the question. While project managers ultimately make simple accept/reject decisions, Roundtable uses granular flags (for example, “Off-topic”, “Gibberish”, and “Low-effort”) to give additional context as to why a response might be low quality.
Second, surveys often face coordinated fraud attempts, either groups submitting similar responses or individuals making multiple submissions. Roundtable’s syntactic analyses identify these patterns by clustering similar responses together. By organizing related responses into groups, researchers can quickly spot coordinated attempts and review entire clusters at once.
Third, AI agents and bots corrupt survey data by injecting automated responses that add noise and bias. To address this, Roundtable uses behavioral biometrics to examine how participants interact with the survey page, analyzing typing patterns, automated entries, and pasted content. These biometrics can detect subtle patterns invisible to human reviewers, identifying automated responses that human review alone might miss. In this way, combining AI pattern detection with human judgment can achieve a higher standard of data quality than either could reach alone.
Rather than making final decisions about response quality, Roundtable is designed to flag potential issues for human review. This approach typically identifies 10-30% of responses for closer examination, saving up to 66% of time spent checking open-ended responses while maintaining high standards.
This efficiency gain doesn't just save time. By reducing the cognitive load of routine quality checks, researchers can devote more attention to analyzing the insights within high-quality responses and make better judgments about edge cases. The result is not only a more efficient process, but a more effective one as well, delivering higher quality data as research projects grow in scale and complexity.
Each layer of Roundtable's system—content analysis, syntactic clustering, and behavioral biometrics—was developed and refined through close collaboration with The Directions Group. The concept of "human-in-the-loop" AI manifests in two crucial ways in this partnership. First, the tools themselves are designed explicitly for human use rather than full automation. Second, as we discuss below, the development of these tools emerges from ongoing collaboration between Roundtable and research experts at The Directions Group.
This process leverages the complementary strengths of both organizations: The Directions Group's research team regularly identifies subtle quality issues that might escape algorithmic detection. Roundtable then conducts deep technical analysis on these cases and incorporates the findings into future system updates. This creates a virtuous cycle where human expertise guides AI development, and improved AI capabilities enable more effective human oversight.
A recent example illustrates the power of this approach. The Directions Group identified a set of suspicious responses to a question about participants’ preferred temperature. These responses contained unusual elements that would be rare in authentic survey responses, such as overly precise estimates, temperature conversions, and degree symbols. These responses had passed Roundtable’s quality checks, but were suspicious to researchers at The Directions Group.
Roundtable's analysis revealed that these responses shared a distinctive pattern: they were entered with unnaturally consistent typing speeds and patterns unlikely to come from human participants. This insight led to the development of a new "unnatural typing" detector, which is now a core part of Roundtable’s data quality toolkit.
This collaborative approach is particularly valuable because data quality is not a static problem. New forms of fraud and quality issues constantly emerge, especially as AI tools become more accessible. The human element in this human-in-the-loop process ensures that detection systems remain responsive to these evolving challenges—expert researchers identify new patterns of suspicious behavior, while AI systems scale this knowledge across millions of responses. Neither technology provider nor research firm operate in isolation. Instead, they form an active partnership that leverages the strengths of both human insight and machine learning.
Artificial intelligence is transforming how businesses operate across every industry. In market research, one of the most promising applications is addressing the longstanding challenge of data quality. However, as the partnership between Roundtable and The Directions Group demonstrates, the most effective solutions don't simply automate existing processes—instead, they fundamentally reimagine how researchers, AI, and technology firms can work together.
Through their ongoing partnership, The Directions Group's researchers can now process more responses in less time, focusing their expertise on the cases that require human judgment. Meanwhile, Roundtable's AI system continuously evolves to address new quality challenges, guided by the insights of experienced research professionals. This approach delivers higher quality data to end clients faster, enabling The Directions Group to meet the growing demands for faster insights with improved quality.
The success of this partnership points to a broader opportunity in market research. As AI capabilities continue to advance, the most successful organizations will be those that find ways to effectively combine human and machine intelligence. The human-in-the-loop approach pioneered by Roundtable and The Directions Group offers a template for this future. In this model, technology providers and research firms work as true partners, continuously learning from each other to deliver better results for their clients.
