Can Generative AI Be Trusted With Numbers? Ensuring Rigour& Reproducibility in Quantitative Research

Описание: By Guillaume Aimetti , Co-founder / CTO, Inspirient GmbH

Generative AI has captured enormous attention in survey research, but its application has been uneven. While qualitative tasks such as coding open-ends have seen dramatic gains, quantitative analysis has remained more resistant. The reason is straightforward: quantitative research relies on mathematical precision, reproducibility, and statistical rigour, which are qualities that large language models are notoriously poor at delivering. When business-critical decisions depend on numbers, “approximately correct” is not good enough. This paper addresses the central question: can generative AI be trusted with numbers? Drawing on practical implementations in survey research, we argue that it can, but only if designed with determinism and transparency at its core. The paper contrasts conventional LLM approaches, which generate text probabilistically, with a hybrid architecture that embeds validated statistical methods within an autonomous AI system. Rather than producing answers that “sound right,” such systems produce findings that can be independently verified, replicated, and trusted. We will review the methodological principles underpinning this approach, including automated crosstabulations, significance testing, regressions, and anomaly detection. Special attention will be given to how the system prioritises meaningful findings, avoiding the pitfalls of surface-level dashboards while reducing analysis time from weeks to minutes. Case studies from organisations such as De Beers, Bose, and leading agencies illustrate how rigorous automation changes practice: from improving data quality, to accelerating delivery, to supporting exploratory analysis. Beyond technical detail, the paper reflects on broader implications for the research industry. If reproducible quant insights can be produced at speed and scale, what does this mean for the role of analysts? How can insight teams ensure that automation enhances, rather than erodes, their professional standards? And how might we reconcile the flexibility of generative AI with the discipline of statistical science?

In addressing these questions, the paper contributes to an urgent debate. Generative AI promises efficiency and accessibility, but only by ensuring rigour and reproducibility can it be trusted as a foundation for quantitative research.