AI to transform qualitative research into measurable data

Mercury Analytics has set out a new view on how organisations will handle qualitative research in 2026, arguing that advances in AI now allow teams to apply measurement and tracking to open-ended feedback.

The company described a shift in insight work that blends established quantitative approaches with qualitative material such as free-text survey responses and narrative feedback. It said improved AI-driven coding of open-ended responses changes the scale at which teams can analyse text and compare results across groups and time periods.

Research and insight functions have often split work between two main methods. Quantitative research uses structured data that organisations can aggregate and compare. Teams often use it for dashboards, benchmarking, and trend reporting. Qualitative research collects narrative input and captures context and emotion. It has often required manual coding and specialist review. That has limited the size of samples that teams can analyse consistently.

Mercury Analytics said AI changes that balance. It pointed to progress in open-end coding, a process that assigns categories to free-text responses. It said the technology improves consistency across large volumes of text and reduces turnaround time for analysis.

"Looking ahead, advances in AI-powered open-end coding are making it increasingly possible to analyze qualitative data at scale and with greater consistency. This marks a meaningful change in how organizations can generate and apply insight," said Ron Howard, Chief Executive Officer, Mercury Analytics.

Method change

Mercury Analytics described the "quantification of qualitative research" as the attachment of measurable structure to open-ended feedback. It said teams can identify themes in narrative responses and then quantify them. It said that approach supports comparisons and tracking. It also said it reduces the reliance on qualitative findings as purely interpretive or anecdotal.

The company said the boundary between quantitative and qualitative work becomes less rigid when organisations can process text at scale. It said qualitative insight no longer needs to stay limited to small samples or long reporting cycles. It said systematic approaches increase the use of qualitative material in decision-making.

One implication sits with survey and feedback design. Organisations have sometimes limited the number of open-ended questions in employee, customer, and market studies. Many teams have done so because free-text analysis takes time and requires specialist input. Mercury Analytics said easier analysis changes those constraints. It said teams can include more narrative questions without creating a bottleneck in the reporting process.

Howard said that measured theme tracking would become more common as organisations expand their use of narrative input. "Themes can be quantified, compared across segments, and tracked over time. The future of insight will increasingly be built on people's own words, supported by data and interpreted with care," said Howard.

Human review

Mercury Analytics also stressed the ongoing role of human judgement. It said AI can speed up categorisation and improve consistency, but it does not remove the need for expert interpretation. It said teams still need to judge meaning in context and apply findings to business strategy.

The company linked the shift to broader management trends that mix structured performance indicators with unstructured feedback from customers and employees. It said organisations increasingly want decision processes that reflect both traditional metrics and real-world language. It said quantifiable qualitative work fits into reporting frameworks used for strategic planning.

Mercury Analytics said the approach would become more relevant during 2026 as more organisations look for ways to integrate narrative insight into ongoing analytics programmes and standard reporting workflows.