Polling firms including YouGov and Ipsos are increasingly turning to artificial intelligence and large language models to analyze public opinion data, as traditional survey methods struggle with declining response rates and slower turnaround times.
Traditional polling has long relied on structured surveys, but participation has fallen sharply in recent years, with response rates in some cases dropping below five percent. That decline has pushed researchers to look for alternative ways to gauge public sentiment, including automated sentiment analysis that draws on large volumes of existing data rather than relying solely on new survey responses.
Large language models are now being used to process open-ended survey responses at scale, identifying themes and sentiment across thousands of text entries in a fraction of the time it would take human analysts working manually. Some firms are also experimenting with so-called synthetic respondents, digital models designed to simulate how different demographic groups might react to particular policies or news events, as a way to supplement traditional data collection.
Accuracy remains a central concern in this shift. Researchers say model performance depends heavily on the diversity and representativeness of the data used for training, and firms are working to identify and correct for bias in language and sentiment analysis. Projects such as PollCheck have been used to compare AI-driven sentiment data against traditional polling results following actual vote counts, allowing researchers to assess where digital methods are performing well and where further calibration is needed.
The approach is also being tested in real time around upcoming elections. With 136 councils in the UK set to vote on May 7 and roughly 5,000 council seats being contested, AI-driven sentiment tracking is being used to monitor how voter priorities shift in the lead-up to the vote, offering campaigns and analysts a more continuously updated picture than traditional polling can provide.
Not everyone in the industry has embraced the shift. OpinionWay’s CEO has said the firm will not use synthetic respondents in its polling, citing concerns that AI-generated data may not adequately reflect real human behavior or hold public trust in the way traditional methods do. That caution reflects a broader divide within the polling and market research industry, where some firms are eager to adopt real-time, AI-driven tracking while others remain committed to more conventional approaches.
Industry professionals widely agree that human oversight remains essential even as AI tools take on a larger share of the analytical workload. Large language models are capable of producing convincing but inaccurate outputs, a phenomenon often referred to as hallucination, and experts say human reviewers are still needed to catch errors and provide cultural and historical context that automated systems lack.
Ethical and privacy considerations are also shaping how these tools are deployed. The UK’s Information Commissioner’s Office has issued guidance addressing synthetic media and watermarking, aimed at helping researchers protect sensitive data while using AI-based analytical tools.
Industry figures have also emphasized the importance of transparency around how algorithmic models are calibrated and updated, arguing that public trust in polling depends on clear disclosure of those processes.
As the technology matures, most firms appear to be settling on a hybrid approach, combining AI-driven speed and scale with continued human validation, rather than fully replacing traditional polling methods.
The balance between efficiency and reliability is likely to remain a central question as more polling organizations adopt these tools ahead of future elections and major public events.
