Article by Phil Ahad – Managing Director, Data at Cint – as originally published in Greenbook
As AI accelerates product development and marketing execution, many organizations are quietly bypassing traditional research altogether. The reason, Cint’s Managing Director of Data Phil Ahad argues, isn’t a lack of interest in insights; it’s frustration with speed, cost, and quality. In this piece, Ahad examines the growing risk facing the research industry: losing its role in real-world decision-making if it fails to evolve.
As an industry, we’ve overcomplicated the most inherently human thing possible: People giving their opinions on any given topic. By prioritizing rigid, legacy methodologies over participant experience, we’ve created an ecosystem where the right people are not interested in giving us their opinions, regardless of compensation. Long, repetitive, and uninspired surveys have become the norm, creating a respondent burden that drives away the very nuance we seek.
We have to shift to bring people back to the center of our data collection by removing this burden. The key to making gathering opinions less of a chore is to simplify the data collection process — making it easier, more engaging, and even fun for the participant — while maintaining the rigorous data points necessary for actionable and trustworthy research.
From Extraction to Augmentation
With the sweeping advancement of AI, LLMs, and synthetic research, we finally have the tools to do just that. For decades, researchers felt they had to squeeze a hundred pieces of information out of a single person in a single sitting. Today, that is no longer necessary: we just need the core information and can fill in, with very high accuracy, the rest of it.
The implementation of AI and synthetic data isn’t about replacing humans; it’s about augmenting high-quality human signals. With data derived from human respondents laying the foundation for the models, it is of utmost importance to ensure the responses are of high quality. As they say: garbage in, garbage out. If the underlying human insights aren’t grounded in authentic nuance, we lose the differentiating data points necessary to drive confident decision-making. Additionally, as consumer sentiment is an ever-changing metric to measure, AI data models must be fed updated human-derived insights to maintain real-time accuracy.
The Hybrid Mandate
The future of the industry lies in hybrid research technologies. This is the middle path, blending the speed of synthetic data with the authentic truth of real-time human feedback. The companies that will dominate this era are those that possess both the AI infrastructure and, crucially, access to reliable human insights at scale. By integrating these technologies into a single, cohesive data strategy, we ensure the industry remains relevant.
In a world where AI allows companies to move quicker and more efficiently than ever before – launching products and marketing campaigns in weeks rather than months – our data must arrive at the same velocity. Or else we risk market research being cut out of the equation. If we’re not able to provide actionable data in a fast and cost-effective way, companies will begin to bypass research entirely.
This is the issue we’re fighting right now: Instead of it being faster, cheaper, and easier for researchers to get the data they need, it’s actually increasingly hard to get the right information from the right respondents at the right time to make informed business decisions.
Given that one of the challenges our industry has been facing is an upsurge in demand for more data and insight, outpacing available supply of high-quality respondents, it may seem incongruous that research risks being eliminated from decision-making processes. People do want the data and insights; however, it’s a question of whether the cost and time is worth it. If we continue just to rely on outdated ways of conducting research – and churning through people to conduct that research – we risk losing our seat at that table in the decision-making process.
A Radical Redefinition
It’s time for a radical redefinition of what market research means, not just how it’s performed. For years, our industry has been chasing the technology curve to meet the needs of real-time data collection and real-time decision-making. However, with the advancement of LLMs, this is a rare instance where the technology to advance data collection and speed up time-to-insights already exists; It’s a matter of integrating it into our processes.
The biggest hurdle we face today is not a lack of innovation; it is our own inertia. Our methodology and research processes are struggling to catch up with the technology already at our fingertips.
Over the past century, even as the tools we use have advanced and evolved, the fundamental interaction of a person asking another person a question to conduct surveys and collect data has remained largely the same. AI and synthetic modeling will provide a massive disruption to this engagement.
Changing this dynamic is the most important and hardest task we face. And it won’t happen overnight. We must perform due diligence to determine exactly how much we can leverage from AI and synthetic modeling to close data gaps without sacrificing the credibility of authentic insights. Then, as we build these new capabilities, we can start to look at changing research methodologies and the process of how people engage within the data collection model.
Conclusion
We have no choice. To stay relevant, we have to implement technology that makes the data gathering process faster and better – for researchers and respondents alike. If we continue to rely on outdated methods and churn through respondents, we will be replaced by faster, albeit shallower, data streams. By embracing the hybrid future, we not only keep our seat at the table; we become the most important voice in the room.
Check out the original article in Greenbook
