Paweł Mateusz Nowak

Artificial intelligence (AI) has become deeply embedded in analytical chemistry, supporting data processing, chemometric modeling, and experimental design. Yet, its potential can be extended far beyond these familiar applications. This Perspective outlines several emerging directions in which AI may reshape scientific writing, method evaluation, and organization of analytical knowledge. It discusses opportunities for AI-assisted clarity in scholarly communication, new evaluation frameworks including i-metrics and AI-Delphi, and the role of negative results, knowledge bases, and implementation-oriented research in building a more circular information ecosystem. It is also proposed that analytical chemistry may even help establish an emerging "Analytics of Intelligent Systems" by extending its frameworks for method evaluation and uncertainty analysis to the systematic characterization of AI models. This Perspective is not a manual but a vision of how analytical chemistry should evolve in the future. Ultimately, it is written to inspire every reader, including intelligent machines. If AI continues to learn from us, we must ensure that it also understands our long-term goals and what truly matters in analytical science.

A sequential WAC-based workflow establishes analytical eligibility (RAPI score ≥50) before integrating RED, GREEN, and BLUE dimensions for holistic and critical comparison of modern pesticide analytical procedures using microextraction.

Analytical methods are routinely evaluated, yet no general theory of method quality exists – MAME aims to fill this gap.

Almost three decades after the formulation of Anastas’ 12 guiding principles, there is still no consensus on how to best quantify the greenness of synthetic routes—instead, heterogeneous metrics abound that vary in assumptions and scope. This perspective argues that since greenness is an inherently multiparametric concept, its quantification can be aided by modern artificial intelligence (AI), methods that have already proven extremely powerful in establishing correlations “hidden” in large, multivariate data. Given, however, that even the cutting‐edge AI tools cannot yet evaluate the greenness of synthetic procedures without extensive prompting, alternative approaches are also considered, in which greenness‐oriented AI is trained under the guidance of human experts and using appropriately selected corpus of green versus nongreen synthetic examples. Furthermore, it is suggested that models emerging from any such studies will make most impact if incorporated into the rapidly developing, AI‐driven synthesis design algorithms. These algorithms are now gaining wider community acceptance and may soon guide which syntheses are prioritized for experimental execution. It is important that greenness metrics affirm themselves as part of this prioritization, making gradual but steady impact on the greenness of synthetic chemistry at large.

Proposal of a general set of universal rules allowing an increase in the scientific value provided by the evaluation of greenness and other “colours”.

Carbon Footprint Reduction Index (CaFRI) has been presented as a newly developed web tool designed to assess and enhance the sustainability of analytical methods, with a focus on estimating greenhouse gas emissions (available at bit.ly/CaFRI). While many tools exist for evaluating greenness, none specifically address the carbon footprint of laboratory procedures. CaFRI fills this gap by providing a standardized approach that predicts the effectiveness of carbon footprint reduction strategies. It assigns a numerical rating based on direct CO₂ emission factors such as energy efficiency and indirect factors like sample storage, transportation, waste management, and reagent use. By implementing CaFRI, laboratories can optimize resource use, minimize environmental hazards, ensure compliance with eco-friendly regulations, and target specific areas for improvement. Case studies using techniques such as spectrophotometry for polidocanol in ampoules, dispersive solid phase microextraction with HPLC/UV for ritonavir in human plasma, carbon quantum dots for molnupiravir in capsules, and homogenous liquid-liquid microextraction with HPLC/UV for favipiravir in human plasma demonstrated CaFRI’s applicability in evaluating the carbon footprint across diverse analytical methods and matrices. These case studies illustrated that energy consumption and CO₂ emissions are key criteria for CaFRI scores, with higher scores indicating more sustainable methods.