What Will Analytical Science Look Like in 2036? Part One: The AI Age

Members of The Analytical Scientist’s 2026 Top 40 Under 40 consider how AI could reshape data interpretation, method development, instrumentation, and the work of analytical scientists – while warning against sacrificing understanding, rigor, or curiosity

October 14, 2026
7 min

The Analytical Scientist

Overview

Experts interviewed by The Analytical Scientist anticipate broader integration of AI across analytical science, including experimental design, instrument operation, data analysis, and reporting. They describe potential workflows in which analytical results guide subsequent experiments, while emphasizing the value of structured experimental knowledge and continued human scientific judgment.

Background

Analytical science increasingly involves complex data, including multi-omics datasets, and diverse therapeutic modalities that require detailed characterization. The article’s contributors describe AI as a developing tool for navigating scientific literature, analyzing data, and connecting experimental planning with execution. They also identify autonomous laboratories and real-time instrument adaptation as possible directions for future analytical workflows. These are expert perspectives about the field’s future, rather than reported clinical trial findings.

Data Highlights

The article presents expert forecasts and examples but reports no numerical results or clinical trial data.

Key Findings

Daniel Petras anticipates AI integration across analytical science, from instrument development and experimental design to reporting and contextualizing studies.
Tao Chen describes a prospective shift toward high-throughput, high-resolution, multi-attribute data to train predictive AI models and support “lab-in-the-loop” workflows.
Bob Pirok argues that autonomous laboratories could use shared, structured knowledge of prior experiments to make method development more reproducible, transferable, and efficient.
Christopher P. Rüger identifies technology development, experimental design and instrument operation, and data analysis and interpretation as areas where AI could affect mass spectrometry.
Rüger describes the possibility of instruments adapting measurements in response to unusual observations during an experiment; this is presented as a future prospect.
A related article frames automation, robotics, and AI as technologies expected to increase efficiency, flexibility, and connectivity in analytical laboratories, while another asks whether AI could replace analytical scientists.

Clinical Implications

The source concerns analytical science broadly and does not provide clinical recommendations or evidence of effects on patient outcomes. Its contributors describe potential research workflows in which AI supports data interpretation and experimental decisions, while presenting human scientific inquiry as continuing to matter.

Conclusion

The article’s contributors expect AI to become more integrated into analytical science, potentially linking data generation, interpretation, and experimental planning. The extent and form of that change remain uncertain in these expert forecasts.